Imported CS 441 audio/transcripts

CS_441_2023_Spring_February_02,_2023.mp3

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CS_441_2023_Spring_February_09,_2023.mp3

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CS_441_2023_Spring_February_14,_2023.mp3

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CS_441_2023_Spring_February_16,_2023.mp3

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CS_441_2023_Spring_February_21,_2023.mp3

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CS_441_2023_Spring_February_23,_2023.mp3

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CS_441_2023_Spring_February_28,_2023.mp3

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CS_441_2023_Spring_January_17,_2023.mp3

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CS_441_2023_Spring_January_17,_2023.vtt

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+WEBVTT Kind: captions; Language: en-US
+
+NOTE
+Created on 2024-02-07T20:43:56.2906687Z by ClassTranscribe
+
+00:03:01.710 --> 00:03:04.290
+Alright, good morning everybody.
+
+00:03:06.080 --> 00:03:07.190
+It's good to see you all.
+
+00:03:07.190 --> 00:03:10.625
+Hope you had a good break, so I'm going
+
+00:03:10.625 --> 00:03:11.430
+to get started.
+
+00:03:12.600 --> 00:03:15.170
+So this is CS441 Applied machine
+
+00:03:15.170 --> 00:03:17.340
+learning and I'm Derek Hoiem, the
+
+00:03:17.340 --> 00:03:17.930
+Instructor.
+
+00:03:19.320 --> 00:03:21.240
+So today I'm going to just tell you a
+
+00:03:21.240 --> 00:03:22.510
+little bit about myself.
+
+00:03:22.510 --> 00:03:24.120
+I'll talk about machine learning,
+
+00:03:24.120 --> 00:03:27.030
+applied machine learning and a bit
+
+00:03:27.030 --> 00:03:28.530
+about the course.
+
+00:03:28.530 --> 00:03:30.616
+I'll give an outline in the course and
+
+00:03:30.616 --> 00:03:32.376
+talk about some of the logistics of the
+
+00:03:32.376 --> 00:03:32.579
+course.
+
+00:03:33.190 --> 00:03:34.510
+And we'll probably end a little bit
+
+00:03:34.510 --> 00:03:36.480
+early so that there's time for you to
+
+00:03:36.480 --> 00:03:37.840
+ask any questions that you have about
+
+00:03:37.840 --> 00:03:41.390
+the course, either individually or you
+
+00:03:41.390 --> 00:03:44.247
+can ask them at the ask them in general
+
+00:03:44.247 --> 00:03:45.880
+at the end, at the end of class.
+
+00:03:47.860 --> 00:03:50.440
+So first a little bit about me.
+
+00:03:50.440 --> 00:03:51.670
+You might know some of this if you've
+
+00:03:51.670 --> 00:03:53.890
+taken a class with me before, but I was
+
+00:03:53.890 --> 00:03:55.800
+raised in upstate New York, right where
+
+00:03:55.800 --> 00:03:57.240
+the circle is and the Hudson River
+
+00:03:57.240 --> 00:03:57.630
+valley.
+
+00:03:57.630 --> 00:03:59.320
+This is the lake that I grew up on.
+
+00:04:00.610 --> 00:04:03.730
+I actually was interested in machine
+
+00:04:03.730 --> 00:04:06.380
+learning and AI for quite a long time.
+
+00:04:07.750 --> 00:04:09.650
+When I decided to go when I went to
+
+00:04:09.650 --> 00:04:11.730
+undergrad at Buffalo, I decided to
+
+00:04:11.730 --> 00:04:14.280
+major in electrical engineering because
+
+00:04:14.280 --> 00:04:15.420
+I.
+
+00:04:15.820 --> 00:04:17.565
+Because it has a good strong background
+
+00:04:17.565 --> 00:04:20.070
+in math, and I was advised that it's a
+
+00:04:20.070 --> 00:04:21.540
+good foundation for anything else I
+
+00:04:21.540 --> 00:04:22.430
+wanted to do.
+
+00:04:22.430 --> 00:04:25.170
+But my roommate was taking computer
+
+00:04:25.170 --> 00:04:27.490
+science and I liked his Assignments,
+
+00:04:27.490 --> 00:04:29.050
+and I started just doing them for fun
+
+00:04:29.050 --> 00:04:31.610
+and started taking more CS courses.
+
+00:04:31.610 --> 00:04:33.360
+And I kind of made a beeline in my
+
+00:04:33.360 --> 00:04:36.239
+curriculum for AI and machine learning,
+
+00:04:36.240 --> 00:04:38.020
+which we're not nearly as popular.
+
+00:04:38.020 --> 00:04:39.830
+At the time I was there was only one
+
+00:04:39.830 --> 00:04:41.435
+graduate course in machine learning,
+
+00:04:41.435 --> 00:04:43.860
+and I was the only undergraduate who
+
+00:04:43.860 --> 00:04:45.940
+took that course that year.
+
+00:04:47.650 --> 00:04:50.828
+And so I ended up adding on the
+
+00:04:50.828 --> 00:04:52.880
+computer the computer engineering
+
+00:04:52.880 --> 00:04:54.240
+degree as well, just because I had
+
+00:04:54.240 --> 00:04:57.340
+taken so many CS courses that I was
+
+00:04:57.340 --> 00:04:58.890
+just able to do that.
+
+00:05:00.000 --> 00:05:02.010
+When I after I graduated, I went to
+
+00:05:02.010 --> 00:05:03.570
+Carnegie Mellon and I went for
+
+00:05:03.570 --> 00:05:04.410
+Robotics.
+
+00:05:04.600 --> 00:05:08.260
+And I started working with somebody
+
+00:05:08.260 --> 00:05:10.866
+named Henry Schneiderman, who made at
+
+00:05:10.866 --> 00:05:12.500
+the what was at the time the most
+
+00:05:12.500 --> 00:05:15.580
+accurate face detector, and then.
+
+00:05:15.660 --> 00:05:19.260
+After he left to start a company and
+
+00:05:19.260 --> 00:05:21.120
+then I started working with Elisha
+
+00:05:21.120 --> 00:05:24.810
+Frozen Marshalli Bear doing applying
+
+00:05:24.810 --> 00:05:26.770
+machine learning to try to recognize
+
+00:05:26.770 --> 00:05:29.430
+geometry to create 3D models based on
+
+00:05:29.430 --> 00:05:31.210
+single view recognition.
+
+00:05:32.140 --> 00:05:35.175
+Then, after my PhD, I came here.
+
+00:05:35.175 --> 00:05:38.280
+I did like a postdoc fellowship for a
+
+00:05:38.280 --> 00:05:39.360
+little bit, and then I've been a
+
+00:05:39.360 --> 00:05:40.720
+professor here ever since.
+
+00:05:43.140 --> 00:05:44.780
+So I'll tell you a little bit about my
+
+00:05:44.780 --> 00:05:45.530
+research.
+
+00:05:45.530 --> 00:05:47.870
+Actually the first two projects that I
+
+00:05:47.870 --> 00:05:51.480
+did were on music identification and
+
+00:05:51.480 --> 00:05:54.440
+sound detection and then this is the.
+
+00:05:54.440 --> 00:05:57.460
+And then I did another one on object on
+
+00:05:57.460 --> 00:05:59.820
+retrieving images based on objects in
+
+00:05:59.820 --> 00:06:00.050
+them.
+
+00:06:00.620 --> 00:06:02.440
+And then this was like my first main
+
+00:06:02.440 --> 00:06:04.970
+project which was part of my thesis.
+
+00:06:04.970 --> 00:06:08.310
+So at the time, if you wanted to create
+
+00:06:08.310 --> 00:06:10.760
+a 3D model of a scene from images, you
+
+00:06:10.760 --> 00:06:12.710
+basically solved a bunch of algebraic
+
+00:06:12.710 --> 00:06:13.460
+constraints.
+
+00:06:14.160 --> 00:06:15.860
+Based on correspondences between the
+
+00:06:15.860 --> 00:06:16.630
+images.
+
+00:06:16.630 --> 00:06:19.680
+But we had this idea that since people
+
+00:06:19.680 --> 00:06:22.010
+are able to see an image like this one
+
+00:06:22.010 --> 00:06:24.823
+and understand the 3D scene behind the
+
+00:06:24.823 --> 00:06:26.650
+image, maybe we can use machine
+
+00:06:26.650 --> 00:06:29.090
+learning to recognize the surfaces and
+
+00:06:29.090 --> 00:06:31.540
+the image and then use that to create a
+
+00:06:31.540 --> 00:06:32.650
+simple 3D model.
+
+00:06:35.370 --> 00:06:37.410
+Currently a couple of the main
+
+00:06:37.410 --> 00:06:40.625
+directions I work in are one of them is
+
+00:06:40.625 --> 00:06:42.070
+this thing called neural radiance
+
+00:06:42.070 --> 00:06:44.610
+fields, and basically the idea is to
+
+00:06:44.610 --> 00:06:47.360
+use the machine learning system as a
+
+00:06:47.360 --> 00:06:50.000
+kind of compression to encode all the
+
+00:06:50.000 --> 00:06:51.905
+geometry and appearance information in
+
+00:06:51.905 --> 00:06:54.525
+the scene so that you can render out
+
+00:06:54.525 --> 00:06:57.895
+the surface normals or the depth or the
+
+00:06:57.895 --> 00:06:59.940
+appearance of images from arbitrary
+
+00:06:59.940 --> 00:07:00.277
+viewpoints.
+
+00:07:00.277 --> 00:07:02.973
+And so this is one of the directions
+
+00:07:02.973 --> 00:07:03.830
+that we're working.
+
+00:07:04.500 --> 00:07:06.470
+And another one is what I call general
+
+00:07:06.470 --> 00:07:07.650
+purpose Learning.
+
+00:07:07.960 --> 00:07:10.930
+And as you'll see, in a lot of machine
+
+00:07:10.930 --> 00:07:13.190
+learning, the goal is to solve one
+
+00:07:13.190 --> 00:07:16.620
+particular kind of Prediction task, or
+
+00:07:16.620 --> 00:07:20.120
+like unsupervised learning task.
+
+00:07:20.120 --> 00:07:22.525
+But people were quite differently.
+
+00:07:22.525 --> 00:07:25.250
+We don't have like a single classifier
+
+00:07:25.250 --> 00:07:27.905
+in our head or a single purpose that
+
+00:07:27.905 --> 00:07:29.900
+our mind is designed for.
+
+00:07:29.900 --> 00:07:32.916
+Instead, we have the ability to sense a
+
+00:07:32.916 --> 00:07:34.638
+lot of things and then we can do a lot
+
+00:07:34.638 --> 00:07:36.650
+of things with our body and our speech,
+
+00:07:36.650 --> 00:07:38.530
+and so we're able to solve kind of an
+
+00:07:38.530 --> 00:07:39.310
+infinite range.
+
+00:07:39.400 --> 00:07:43.100
+Tasks that are senses and our actions
+
+00:07:43.100 --> 00:07:44.450
+allow us to perform.
+
+00:07:44.450 --> 00:07:46.140
+And so we're trying to do the same
+
+00:07:46.140 --> 00:07:47.790
+thing for machine learning, to create
+
+00:07:47.790 --> 00:07:50.493
+systems that are able to receive some
+
+00:07:50.493 --> 00:07:53.538
+kinds of inputs, like text and images,
+
+00:07:53.538 --> 00:07:56.330
+and produce outputs which could also be
+
+00:07:56.330 --> 00:07:57.457
+like text or images.
+
+00:07:57.457 --> 00:08:00.180
+And then do any kinds of tasks that
+
+00:08:00.180 --> 00:08:02.040
+fall within that range of the input and
+
+00:08:02.040 --> 00:08:02.833
+output modality.
+
+00:08:02.833 --> 00:08:05.240
+And then we also work on ways to try to
+
+00:08:05.240 --> 00:08:08.380
+extend these systems, for example in
+
+00:08:08.380 --> 00:08:08.750
+this.
+
+00:08:09.410 --> 00:08:11.260
+And this slide here is showing that we
+
+00:08:11.260 --> 00:08:14.000
+created this system that is able to map
+
+00:08:14.000 --> 00:08:16.420
+from images in a text prompt into some
+
+00:08:16.420 --> 00:08:17.320
+kind of answer.
+
+00:08:18.080 --> 00:08:20.260
+And then the system is able to learn
+
+00:08:20.260 --> 00:08:23.470
+from web images to learn new concepts.
+
+00:08:23.470 --> 00:08:25.550
+So we were able to, we provided it with
+
+00:08:25.550 --> 00:08:27.682
+like a set of keywords about COVID, and
+
+00:08:27.682 --> 00:08:29.760
+then it was able to just download a
+
+00:08:29.760 --> 00:08:31.863
+bunch of images from Bing and then
+
+00:08:31.863 --> 00:08:33.530
+learn how to answer questions and
+
+00:08:33.530 --> 00:08:35.300
+caption and detect things that are
+
+00:08:35.300 --> 00:08:37.290
+related to COVID, which we're not in
+
+00:08:37.290 --> 00:08:38.795
+any of the original data that I trained
+
+00:08:38.795 --> 00:08:39.050
+in.
+
+00:08:42.460 --> 00:08:44.460
+I've also used machine learning in a
+
+00:08:44.460 --> 00:08:47.414
+lot of other ways, so let me just.
+
+00:08:47.414 --> 00:08:49.045
+I just want to make sure I think the
+
+00:08:49.045 --> 00:08:50.650
+mic's working, but I just want to.
+
+00:08:51.720 --> 00:08:54.230
+Make sure that it's OK.
+
+00:08:54.230 --> 00:08:56.480
+It'll pick up a little better down here
+
+00:08:56.480 --> 00:08:58.370
+because for the recording.
+
+00:09:00.030 --> 00:09:02.375
+So I've used machine learning in lots
+
+00:09:02.375 --> 00:09:02.960
+of different ways.
+
+00:09:02.960 --> 00:09:05.540
+In my research I've done things like
+
+00:09:05.540 --> 00:09:07.515
+object detection, image classification,
+
+00:09:07.515 --> 00:09:10.020
+3D scene modeling, Generating
+
+00:09:10.020 --> 00:09:12.300
+animations, visual question answering,
+
+00:09:12.300 --> 00:09:14.240
+phrase grounding, sound detection.
+
+00:09:14.240 --> 00:09:16.690
+So lots of different, lots of different
+
+00:09:16.690 --> 00:09:17.390
+use cases.
+
+00:09:18.850 --> 00:09:20.566
+And then I've also used machine
+
+00:09:20.566 --> 00:09:22.700
+learning in Application.
+
+00:09:22.700 --> 00:09:25.240
+So I cofounded this company,
+
+00:09:25.240 --> 00:09:27.420
+Reconstruct, where we take images of a
+
+00:09:27.420 --> 00:09:29.460
+construction site and bring it together
+
+00:09:29.460 --> 00:09:31.525
+with building plans and schedule to
+
+00:09:31.525 --> 00:09:32.955
+help all the stakeholders understand
+
+00:09:32.955 --> 00:09:35.480
+the progress and whether things are
+
+00:09:35.480 --> 00:09:37.230
+being built according to the plan.
+
+00:09:37.230 --> 00:09:40.694
+And part of this, some of the 3D
+
+00:09:40.694 --> 00:09:42.020
+construction doesn't use machine
+
+00:09:42.020 --> 00:09:43.990
+learning, but some of it does to help
+
+00:09:43.990 --> 00:09:47.170
+create more complete Models and then to
+
+00:09:47.170 --> 00:09:48.880
+recognize things in the scene.
+
+00:09:48.950 --> 00:09:51.060
+To help monitor their progress.
+
+00:09:53.330 --> 00:09:56.100
+So all of that is to say that I've had
+
+00:09:56.100 --> 00:09:58.100
+quite a lot of experience using machine
+
+00:09:58.100 --> 00:09:59.380
+learning in a variety of different
+
+00:09:59.380 --> 00:10:02.205
+ways, many different modalities, many
+
+00:10:02.205 --> 00:10:04.940
+different kinds of applications, both
+
+00:10:04.940 --> 00:10:08.220
+research and in commercial types of
+
+00:10:08.220 --> 00:10:08.620
+applications.
+
+00:10:10.360 --> 00:10:12.267
+So what is machine learning?
+
+00:10:12.267 --> 00:10:14.170
+So everyone has their own definition.
+
+00:10:14.170 --> 00:10:16.740
+But the way that I think about it is
+
+00:10:16.740 --> 00:10:19.195
+that it's machine learning spins Raw
+
+00:10:19.195 --> 00:10:21.710
+data into gold, so it's pretty easy to
+
+00:10:21.710 --> 00:10:22.180
+get data.
+
+00:10:22.180 --> 00:10:23.880
+Now there's all kinds of data.
+
+00:10:23.880 --> 00:10:26.210
+Whenever you use applications, you're
+
+00:10:26.210 --> 00:10:29.500
+providing those Application providers
+
+00:10:29.500 --> 00:10:30.900
+with your data.
+
+00:10:30.900 --> 00:10:33.180
+A lot of times you can download lots of
+
+00:10:33.180 --> 00:10:34.470
+information on the Internet.
+
+00:10:34.470 --> 00:10:36.850
+There's just data everywhere, but by
+
+00:10:36.850 --> 00:10:38.970
+itself, that data is not very useful.
+
+00:10:39.030 --> 00:10:41.270
+It's too much to manually go through.
+
+00:10:41.270 --> 00:10:42.610
+It's not something that you can
+
+00:10:42.610 --> 00:10:46.320
+actually solve a problem with, and so
+
+00:10:46.320 --> 00:10:50.460
+machine learning is really the ability
+
+00:10:50.460 --> 00:10:52.610
+to take all of that Raw data and turn
+
+00:10:52.610 --> 00:10:55.066
+it into something useful to be able to
+
+00:10:55.066 --> 00:10:57.390
+create predictive models or to create
+
+00:10:57.390 --> 00:10:58.080
+insights.
+
+00:10:58.710 --> 00:11:01.760
+So some examples are here for the Alexa
+
+00:11:01.760 --> 00:11:04.060
+speech recognition where you.
+
+00:11:04.340 --> 00:11:06.940
+Where they learn from audio
+
+00:11:06.940 --> 00:11:09.920
+transcriptions to be able to take the
+
+00:11:09.920 --> 00:11:13.465
+voice information that you speak into
+
+00:11:13.465 --> 00:11:16.235
+the Alexa app and turn it into text and
+
+00:11:16.235 --> 00:11:18.390
+then it's then turned into other
+
+00:11:18.390 --> 00:11:19.310
+information.
+
+00:11:20.450 --> 00:11:23.110
+Or product recommendations?
+
+00:11:23.110 --> 00:11:24.860
+Autonomous vehicles?
+
+00:11:26.330 --> 00:11:30.186
+Text generation like GPT 3 or GPT chat
+
+00:11:30.186 --> 00:11:32.230
+or ChatGPT image generation.
+
+00:11:32.230 --> 00:11:34.675
+And then there's a link there that you
+
+00:11:34.675 --> 00:11:35.580
+can check out later.
+
+00:11:35.580 --> 00:11:37.780
+It's a data visualization of Twitter
+
+00:11:37.780 --> 00:11:40.780
+where trying to take like all the tons
+
+00:11:40.780 --> 00:11:42.400
+of information about different tweets
+
+00:11:42.400 --> 00:11:44.642
+and organize it in a way so that you
+
+00:11:44.642 --> 00:11:47.240
+can draw insights about like social
+
+00:11:47.240 --> 00:11:49.780
+trends and kind of what's going on in
+
+00:11:49.780 --> 00:11:50.630
+different places.
+
+00:11:54.780 --> 00:11:58.300
+So we tend to think in classes.
+
+00:11:58.300 --> 00:12:00.710
+Especially we tend to think about
+
+00:12:00.710 --> 00:12:03.250
+machine learning as a problem of
+
+00:12:03.250 --> 00:12:09.216
+designing algorithms that will map your
+
+00:12:09.216 --> 00:12:11.900
+that allow you to learn from the
+
+00:12:11.900 --> 00:12:14.410
+training data and to achieve good test
+
+00:12:14.410 --> 00:12:16.880
+performance in your Prediction task
+
+00:12:16.880 --> 00:12:18.150
+according to the test data.
+
+00:12:18.940 --> 00:12:20.920
+But in the real world, there's actually
+
+00:12:20.920 --> 00:12:22.310
+a bigger problem.
+
+00:12:22.310 --> 00:12:24.646
+There's a bigger infrastructure around
+
+00:12:24.646 --> 00:12:25.518
+machine learning.
+
+00:12:25.518 --> 00:12:27.945
+And so while we're going to focus on
+
+00:12:27.945 --> 00:12:30.240
+the Algorithm and model development,
+
+00:12:30.240 --> 00:12:32.099
+it's also important to be aware of the
+
+00:12:32.100 --> 00:12:34.154
+broader context of machine learning.
+
+00:12:34.154 --> 00:12:36.800
+So if you were to ever go work for a
+
+00:12:36.800 --> 00:12:39.465
+company and they say that they want you
+
+00:12:39.465 --> 00:12:43.100
+to build some kind of predictor or some
+
+00:12:43.100 --> 00:12:45.583
+data cluster or something like that,
+
+00:12:45.583 --> 00:12:48.230
+you would actually go through multiple
+
+00:12:48.230 --> 00:12:49.590
+different phases, so the 1st.
+
+00:12:50.360 --> 00:12:52.445
+And potentially the most important is
+
+00:12:52.445 --> 00:12:54.710
+the data preparation where you collect
+
+00:12:54.710 --> 00:12:56.326
+and curate the data.
+
+00:12:56.326 --> 00:12:59.147
+So you have to find examples you have.
+
+00:12:59.147 --> 00:13:00.970
+You may need to Annotate it or hire
+
+00:13:00.970 --> 00:13:03.370
+annotators or create annotation tools.
+
+00:13:03.370 --> 00:13:05.858
+And then you split your data often into
+
+00:13:05.858 --> 00:13:07.740
+a training set, validation set and a
+
+00:13:07.740 --> 00:13:08.278
+test set.
+
+00:13:08.278 --> 00:13:10.350
+So the training set is what you would
+
+00:13:10.350 --> 00:13:11.938
+use to tune your Models, the validation
+
+00:13:11.938 --> 00:13:14.441
+is what you use to select your Models,
+
+00:13:14.441 --> 00:13:17.685
+and the test set is for your final
+
+00:13:17.685 --> 00:13:20.210
+Final like estimate of your systems.
+
+00:13:20.270 --> 00:13:21.060
+Performance.
+
+00:13:22.370 --> 00:13:23.920
+Then once you have that, then you can
+
+00:13:23.920 --> 00:13:25.115
+develop your algorithm.
+
+00:13:25.115 --> 00:13:27.560
+You can decide what kind of machine
+
+00:13:27.560 --> 00:13:29.010
+learning algorithm you're going to use.
+
+00:13:29.010 --> 00:13:30.560
+What are the hyperparameters.
+
+00:13:31.570 --> 00:13:33.520
+What kinds of objectives you're going
+
+00:13:33.520 --> 00:13:37.120
+to give to your Algorithm and you train
+
+00:13:37.120 --> 00:13:38.850
+it and evaluate it?
+
+00:13:38.850 --> 00:13:41.350
+Often that process takes quite a lot of
+
+00:13:41.350 --> 00:13:44.193
+time and it may lead you to go back to
+
+00:13:44.193 --> 00:13:46.050
+the data preparation stage if you
+
+00:13:46.050 --> 00:13:47.400
+realize that you're not going to be
+
+00:13:47.400 --> 00:13:49.960
+able to reach the applications
+
+00:13:49.960 --> 00:13:51.030
+performance requirements.
+
+00:13:52.350 --> 00:13:54.100
+Once you're finally feel like you've
+
+00:13:54.100 --> 00:13:55.922
+finished developing the Algorithm, then
+
+00:13:55.922 --> 00:13:58.430
+you would test it on a test set, which
+
+00:13:58.430 --> 00:14:00.230
+ideally you haven't used at any stage
+
+00:14:00.230 --> 00:14:01.520
+before, so that it gives you an
+
+00:14:01.520 --> 00:14:03.480
+unbiased estimate of your performance,
+
+00:14:03.480 --> 00:14:05.640
+and then finally you integrate it into
+
+00:14:05.640 --> 00:14:06.570
+your Application.
+
+00:14:07.210 --> 00:14:08.600
+And.
+
+00:14:08.710 --> 00:14:11.160
+And usually this Prediction engine that
+
+00:14:11.160 --> 00:14:13.170
+you've built will only be one part of
+
+00:14:13.170 --> 00:14:15.410
+an entire solution, and so it needs to
+
+00:14:15.410 --> 00:14:16.853
+work well with the rest of that
+
+00:14:16.853 --> 00:14:17.139
+solution.
+
+00:14:18.420 --> 00:14:22.480
+So as an example, consider the voice
+
+00:14:22.480 --> 00:14:24.685
+recognition and Alexa.
+
+00:14:24.685 --> 00:14:27.896
+So they had a really challenging
+
+00:14:27.896 --> 00:14:30.540
+problem that probably many of you have
+
+00:14:30.540 --> 00:14:34.120
+at least used Alexa app sometime.
+
+00:14:34.120 --> 00:14:35.696
+But there's a really challenging
+
+00:14:35.696 --> 00:14:37.370
+problem that you've got like some disc
+
+00:14:37.370 --> 00:14:40.280
+with some microphones on it and it you
+
+00:14:40.280 --> 00:14:43.945
+need the disk needs to be able to
+
+00:14:43.945 --> 00:14:45.855
+interpret your speech and it needs to
+
+00:14:45.855 --> 00:14:47.690
+be able to interpret speech for a wide
+
+00:14:47.690 --> 00:14:48.850
+range of people.
+
+00:14:49.140 --> 00:14:51.430
+That are not just like talking into it.
+
+00:14:51.430 --> 00:14:53.550
+You might talk into your cell phone,
+
+00:14:53.550 --> 00:14:55.425
+but they could be shouting from across
+
+00:14:55.425 --> 00:14:56.190
+the house.
+
+00:14:56.190 --> 00:14:59.710
+Alexa, turn on turn play The Beatles or
+
+00:14:59.710 --> 00:15:00.870
+Alexa, what's the temperature?
+
+00:15:01.490 --> 00:15:05.090
+And Alexa needs to turn that into text
+
+00:15:05.090 --> 00:15:08.670
+and then use other engines in order to
+
+00:15:08.670 --> 00:15:09.880
+produce an answer that might be
+
+00:15:09.880 --> 00:15:10.980
+relevant question.
+
+00:15:12.790 --> 00:15:13.276
+Yeah.
+
+00:15:13.276 --> 00:15:15.530
+Chat, ChatGPT is another example, but
+
+00:15:15.530 --> 00:15:17.040
+that's quite different, yeah.
+
+00:15:18.220 --> 00:15:23.290
+And so for you could Alexa could turn
+
+00:15:23.290 --> 00:15:23.540
+that.
+
+00:15:23.540 --> 00:15:25.500
+I'm sure that there are ChatGPT apps
+
+00:15:25.500 --> 00:15:26.980
+that are connected to Alexa already
+
+00:15:26.980 --> 00:15:29.610
+where you can then say, Alexa write my
+
+00:15:29.610 --> 00:15:30.650
+essay for me.
+
+00:15:35.280 --> 00:15:36.540
+So.
+
+00:15:37.030 --> 00:15:38.660
+So for so they had this really
+
+00:15:38.660 --> 00:15:40.030
+challenging speech recognition
+
+00:15:40.030 --> 00:15:41.846
+algorithm and they realized that the
+
+00:15:41.846 --> 00:15:43.300
+state-of-the-art wasn't up to the task.
+
+00:15:43.300 --> 00:15:46.400
+So they needed to achieve a certain
+
+00:15:46.400 --> 00:15:48.853
+word error rate, which means that the
+
+00:15:48.853 --> 00:15:51.650
+rate, the error rate for converting the
+
+00:15:51.650 --> 00:15:54.210
+spoken words into text.
+
+00:15:55.090 --> 00:15:57.053
+And they were kind of far below that.
+
+00:15:57.053 --> 00:15:59.120
+And so first, they started buying
+
+00:15:59.120 --> 00:16:01.590
+companies that were developing speech
+
+00:16:01.590 --> 00:16:02.656
+recognition engines.
+
+00:16:02.656 --> 00:16:05.280
+They started using deep learning.
+
+00:16:05.280 --> 00:16:06.930
+They started to try to improve their
+
+00:16:06.930 --> 00:16:10.002
+Algorithms, collect data, Annotate the
+
+00:16:10.002 --> 00:16:10.445
+data.
+
+00:16:10.445 --> 00:16:13.360
+And they had meetings with Jeff Bezos
+
+00:16:13.360 --> 00:16:18.590
+and said, we expect that within 10
+
+00:16:18.590 --> 00:16:20.120
+years, we're going to achieve the word
+
+00:16:20.120 --> 00:16:21.470
+error rate that we need.
+
+00:16:21.470 --> 00:16:23.850
+And basically Jeff Bezos, this was like
+
+00:16:23.850 --> 00:16:25.370
+his favorite project and he's like,
+
+00:16:25.370 --> 00:16:25.580
+well.
+
+00:16:25.630 --> 00:16:27.130
+You guys aren't thinking big enough.
+
+00:16:27.130 --> 00:16:29.220
+Basically we have tons of money.
+
+00:16:29.220 --> 00:16:31.380
+Figure out how you can do it faster.
+
+00:16:31.380 --> 00:16:33.390
+And So what they ended up doing was.
+
+00:16:33.470 --> 00:16:37.040
+Venting Airbnbs in Boston and other
+
+00:16:37.040 --> 00:16:39.710
+places, setting up Alexis, they're
+
+00:16:39.710 --> 00:16:42.488
+creating scripts and hiring people to
+
+00:16:42.488 --> 00:16:44.672
+walk through these Airbnbs reading the
+
+00:16:44.672 --> 00:16:46.495
+scripts, reading a certain script in
+
+00:16:46.495 --> 00:16:49.490
+each room so that way they know what is
+
+00:16:49.490 --> 00:16:51.785
+said in each room, so they have the
+
+00:16:51.785 --> 00:16:52.355
+ground truth.
+
+00:16:52.355 --> 00:16:54.252
+The script is basically the ground
+
+00:16:54.252 --> 00:16:56.470
+truth, and they can automatically train
+
+00:16:56.470 --> 00:16:59.542
+and test their systems on all of these
+
+00:16:59.542 --> 00:17:02.160
+people that they on the voices of all
+
+00:17:02.160 --> 00:17:03.446
+the people they hired to walk through
+
+00:17:03.446 --> 00:17:04.160
+all these different.
+
+00:17:04.220 --> 00:17:04.940
+Apartments.
+
+00:17:04.940 --> 00:17:08.370
+And so they were able to thousand X
+
+00:17:08.370 --> 00:17:11.100
+their training examples and that's how
+
+00:17:11.100 --> 00:17:13.670
+they achieved their robust speech
+
+00:17:13.670 --> 00:17:14.820
+recognition that they have.
+
+00:17:16.400 --> 00:17:20.160
+And so it's kind of the lesson is that
+
+00:17:20.160 --> 00:17:22.430
+machine learning is really, it's not
+
+00:17:22.430 --> 00:17:25.062
+just about developing algorithms or
+
+00:17:25.062 --> 00:17:28.299
+about tuning your losses or tuning your
+
+00:17:28.300 --> 00:17:30.690
+parameters, but it's also about data
+
+00:17:30.690 --> 00:17:32.620
+preparation and the actual deployment
+
+00:17:32.620 --> 00:17:34.420
+of the technology.
+
+00:17:37.840 --> 00:17:39.590
+So this.
+
+00:17:40.850 --> 00:17:42.860
+So again, like in this class, we're
+
+00:17:42.860 --> 00:17:44.690
+going to focus on the Algorithm and
+
+00:17:44.690 --> 00:17:46.610
+model development and partly it's
+
+00:17:46.610 --> 00:17:48.370
+because there's not time to go out and
+
+00:17:48.370 --> 00:17:49.855
+collect lots of data and Annotate it.
+
+00:17:49.855 --> 00:17:51.420
+It's extremely time consuming.
+
+00:17:51.420 --> 00:17:53.306
+So this is what we can focus on and it
+
+00:17:53.306 --> 00:17:55.360
+is the most challenging and technically
+
+00:17:55.360 --> 00:17:58.949
+complex part of the development.
+
+00:18:01.360 --> 00:18:03.910
+A lot of most, if not all, machine
+
+00:18:03.910 --> 00:18:06.270
+learning algorithms fall into this very
+
+00:18:06.270 --> 00:18:07.790
+simple diagram.
+
+00:18:08.550 --> 00:18:10.690
+Where you have some Raw data, it could
+
+00:18:10.690 --> 00:18:13.330
+be text or images or audio or
+
+00:18:13.330 --> 00:18:15.500
+temperatures or other information.
+
+00:18:16.520 --> 00:18:19.466
+Usually that Raw data is not in a form
+
+00:18:19.466 --> 00:18:22.210
+that is very useful for Prediction.
+
+00:18:22.210 --> 00:18:24.502
+For example, if you receive an image,
+
+00:18:24.502 --> 00:18:27.010
+the intensities of the image are just
+
+00:18:27.010 --> 00:18:28.780
+numbers that correspond to how bright
+
+00:18:28.780 --> 00:18:31.410
+each pixel is, and individually those
+
+00:18:31.410 --> 00:18:32.860
+numbers don't really tell you much at
+
+00:18:32.860 --> 00:18:33.530
+all.
+
+00:18:33.530 --> 00:18:35.296
+What's more meaningful are the
+
+00:18:35.296 --> 00:18:37.550
+patterns, the local patterns of the
+
+00:18:37.550 --> 00:18:37.950
+intensities.
+
+00:18:38.810 --> 00:18:40.590
+And so you need to have some kind of
+
+00:18:40.590 --> 00:18:44.110
+encoding of that Raw data that makes
+
+00:18:44.110 --> 00:18:45.370
+the data more meaningful.
+
+00:18:46.340 --> 00:18:49.240
+And there's lots of different ways that
+
+00:18:49.240 --> 00:18:50.530
+you can create that encoding.
+
+00:18:50.530 --> 00:18:52.315
+You can manually define features, you
+
+00:18:52.315 --> 00:18:54.870
+could have Trees, you could do
+
+00:18:54.870 --> 00:18:57.060
+Probabilistic estimation, or use Deep
+
+00:18:57.060 --> 00:18:57.850
+networks.
+
+00:18:58.600 --> 00:18:59.715
+Then you have a Decoder.
+
+00:18:59.715 --> 00:19:01.210
+The Decoder takes your encoded
+
+00:19:01.210 --> 00:19:03.480
+representation and then it tries to
+
+00:19:03.480 --> 00:19:05.060
+produce something useful from it.
+
+00:19:05.060 --> 00:19:07.120
+Some Prediction that you want classify
+
+00:19:07.120 --> 00:19:10.910
+an image or convert the raw audio into
+
+00:19:10.910 --> 00:19:11.390
+text.
+
+00:19:12.680 --> 00:19:14.663
+Again, there's lots of decoders.
+
+00:19:14.663 --> 00:19:16.790
+There's actually not so much complexity
+
+00:19:16.790 --> 00:19:18.910
+usually in the decoders, but some of
+
+00:19:18.910 --> 00:19:20.800
+the common ones are nearest neighbor or
+
+00:19:20.800 --> 00:19:24.190
+Linear decoders, an SVM or logistic,
+
+00:19:24.190 --> 00:19:25.220
+logistic regressor.
+
+00:19:26.960 --> 00:19:28.560
+Then the decoders will produce some
+
+00:19:28.560 --> 00:19:30.630
+Prediction from the encoded Raw data.
+
+00:19:30.630 --> 00:19:32.480
+And again, there's lots of different
+
+00:19:32.480 --> 00:19:33.670
+kinds of problems out there, so there
+
+00:19:33.670 --> 00:19:34.850
+are many different things that you
+
+00:19:34.850 --> 00:19:35.820
+might be trying to predict.
+
+00:19:35.820 --> 00:19:37.610
+Could be binary labels, you could be
+
+00:19:37.610 --> 00:19:39.710
+producing an image, or producing Text,
+
+00:19:39.710 --> 00:19:41.690
+or detecting objects.
+
+00:19:42.620 --> 00:19:44.666
+When you're training, you'll also have
+
+00:19:44.666 --> 00:19:46.330
+some target Labels, so you have
+
+00:19:46.330 --> 00:19:47.000
+annotated data.
+
+00:19:47.000 --> 00:19:48.390
+If you have a Supervised Algorithm
+
+00:19:48.390 --> 00:19:51.150
+anyway, you have some annotated data
+
+00:19:51.150 --> 00:19:52.426
+where you have the Raw data and you
+
+00:19:52.426 --> 00:19:53.580
+have the labels that you want to
+
+00:19:53.580 --> 00:19:54.390
+predict.
+
+00:19:54.390 --> 00:19:56.410
+You see how different your target
+
+00:19:56.410 --> 00:19:58.650
+Labels are from the predictions, and
+
+00:19:58.650 --> 00:20:00.390
+then based on that you.
+
+00:20:01.190 --> 00:20:04.225
+Improve your Decoder, and in some cases
+
+00:20:04.225 --> 00:20:06.150
+you can then feed that back into your
+
+00:20:06.150 --> 00:20:08.600
+Encoder to improve your encoding
+
+00:20:08.600 --> 00:20:10.270
+representation.
+
+00:20:10.270 --> 00:20:12.520
+So there's a single process that is
+
+00:20:12.520 --> 00:20:14.890
+used by almost all of machine learning,
+
+00:20:14.890 --> 00:20:15.990
+but there's a lot of different
+
+00:20:15.990 --> 00:20:17.850
+variation and a lot of different
+
+00:20:17.850 --> 00:20:18.600
+details.
+
+00:20:18.600 --> 00:20:20.414
+And that's just because of all the
+
+00:20:20.414 --> 00:20:21.897
+different kinds of Raw data that you
+
+00:20:21.897 --> 00:20:23.864
+could be dealing with in all the
+
+00:20:23.864 --> 00:20:25.213
+different kinds of predictions that you
+
+00:20:25.213 --> 00:20:25.730
+could do.
+
+00:20:29.380 --> 00:20:31.157
+So that's machine learning.
+
+00:20:31.157 --> 00:20:33.140
+That's machine learning in general.
+
+00:20:33.140 --> 00:20:34.470
+And so now I'm going to talk a little
+
+00:20:34.470 --> 00:20:36.020
+bit about some of the details of the
+
+00:20:36.020 --> 00:20:36.730
+course.
+
+00:20:37.780 --> 00:20:40.500
+So one of the one of the main course
+
+00:20:40.500 --> 00:20:42.680
+objectives is to try to learn how to
+
+00:20:42.680 --> 00:20:44.420
+solve problems with machine learning.
+
+00:20:45.110 --> 00:20:47.990
+So this involves trying to learn the
+
+00:20:47.990 --> 00:20:50.132
+key concepts and methodologies for how
+
+00:20:50.132 --> 00:20:51.140
+we can learn from data.
+
+00:20:51.140 --> 00:20:53.170
+We're going to learn about a wide
+
+00:20:53.170 --> 00:20:56.230
+variety of algorithms and what they're
+
+00:20:56.230 --> 00:20:57.890
+strengths and limitations are.
+
+00:20:57.890 --> 00:21:00.110
+And we're also going to learn about
+
+00:21:00.110 --> 00:21:02.138
+domain specific representations like
+
+00:21:02.138 --> 00:21:04.090
+how we can encode images, how we can
+
+00:21:04.090 --> 00:21:06.220
+encode text, how we can encode audio.
+
+00:21:06.220 --> 00:21:10.095
+And then we want to at the end of this
+
+00:21:10.095 --> 00:21:11.540
+have the ability to select the right
+
+00:21:11.540 --> 00:21:13.870
+tools for the job so that if you have
+
+00:21:13.870 --> 00:21:15.530
+your own custom problem that.
+
+00:21:15.580 --> 00:21:17.180
+You want to solve that.
+
+00:21:17.180 --> 00:21:19.270
+You're able to make good choices about
+
+00:21:19.270 --> 00:21:22.000
+how to represent the data and what
+
+00:21:22.000 --> 00:21:24.160
+kinds of algorithms and losses to use,
+
+00:21:24.160 --> 00:21:26.812
+how to optimize your algorithms, and
+
+00:21:26.812 --> 00:21:29.140
+how to solve your problem.
+
+00:21:31.010 --> 00:21:34.049
+So I think this is, I think it's just
+
+00:21:34.050 --> 00:21:36.510
+generally interesting to be able to do
+
+00:21:36.510 --> 00:21:38.450
+this, but it's also very practical.
+
+00:21:38.450 --> 00:21:41.280
+One example is just if you look at it
+
+00:21:41.280 --> 00:21:43.880
+in terms of the number of jobs that are
+
+00:21:43.880 --> 00:21:45.930
+available in the demand for people that
+
+00:21:45.930 --> 00:21:48.250
+have skills in machine learning
+
+00:21:48.250 --> 00:21:50.810
+engineering, it's an area that's been
+
+00:21:50.810 --> 00:21:52.260
+growing really rapidly.
+
+00:21:52.260 --> 00:21:55.469
+So according to this, according to this
+
+00:21:55.470 --> 00:21:55.910
+source.
+
+00:21:56.540 --> 00:21:58.220
+The.
+
+00:21:58.310 --> 00:22:00.380
+Market for global machine learning is
+
+00:22:00.380 --> 00:22:02.860
+expected to grow from around 20 billion
+
+00:22:02.860 --> 00:22:07.230
+last year to 200 billion in 2029, which
+
+00:22:07.230 --> 00:22:09.080
+is a growth rate of about 38%.
+
+00:22:09.080 --> 00:22:11.340
+So machine learning engineers are in
+
+00:22:11.340 --> 00:22:13.300
+high demand now and they're going to
+
+00:22:13.300 --> 00:22:14.959
+likely continue to be in high demand
+
+00:22:14.960 --> 00:22:16.340
+over the next several years.
+
+00:22:18.860 --> 00:22:21.540
+Another objective of the course is to
+
+00:22:21.540 --> 00:22:23.010
+have a better understanding of the real
+
+00:22:23.010 --> 00:22:25.150
+life applications and the social
+
+00:22:25.150 --> 00:22:26.758
+implications of machine learning.
+
+00:22:26.758 --> 00:22:29.330
+So I'll try to talk about actual
+
+00:22:29.330 --> 00:22:30.720
+deployments and machine learning and
+
+00:22:30.720 --> 00:22:31.712
+several cases.
+
+00:22:31.712 --> 00:22:35.870
+We'll talk about some of the ethical
+
+00:22:35.870 --> 00:22:37.479
+concerns around machine learning and
+
+00:22:37.480 --> 00:22:40.899
+its use, and about the and about
+
+00:22:40.900 --> 00:22:42.390
+different application domains.
+
+00:22:42.390 --> 00:22:44.440
+So machine learning can do a lot of
+
+00:22:44.440 --> 00:22:45.050
+good.
+
+00:22:45.050 --> 00:22:46.800
+There's a lot of ways that.
+
+00:22:46.860 --> 00:22:49.170
+Already impacts our lives through our
+
+00:22:49.170 --> 00:22:52.330
+cameras, through through Smart
+
+00:22:52.330 --> 00:22:54.283
+assistants and things like that.
+
+00:22:54.283 --> 00:22:58.193
+Of course, technology can also create a
+
+00:22:58.193 --> 00:23:00.453
+lot of upheaval and also can do a lot
+
+00:23:00.453 --> 00:23:01.230
+of damage.
+
+00:23:01.230 --> 00:23:03.820
+And so it's important to understand the
+
+00:23:03.820 --> 00:23:06.390
+implications of the technology that we
+
+00:23:06.390 --> 00:23:06.940
+develop.
+
+00:23:09.670 --> 00:23:11.630
+And then the third is appreciation for
+
+00:23:11.630 --> 00:23:13.160
+your own constantly Learning minds.
+
+00:23:13.160 --> 00:23:15.480
+So we're humans are still the best
+
+00:23:15.480 --> 00:23:17.180
+machine Learners, I would say.
+
+00:23:18.290 --> 00:23:20.490
+You're always you're always learning
+
+00:23:20.490 --> 00:23:21.370
+new things.
+
+00:23:21.370 --> 00:23:23.170
+You're always learning about new
+
+00:23:23.170 --> 00:23:24.140
+environments.
+
+00:23:24.140 --> 00:23:27.730
+You learn new skills, new facts, new
+
+00:23:27.730 --> 00:23:28.720
+concepts.
+
+00:23:28.720 --> 00:23:34.666
+And the flexibility of our Learning and
+
+00:23:34.666 --> 00:23:37.140
+the breadth of it and the seamlessness
+
+00:23:37.140 --> 00:23:39.040
+of it is really amazing.
+
+00:23:39.040 --> 00:23:42.420
+And so as you learn how, even though we
+
+00:23:42.420 --> 00:23:46.290
+can do pretty cool things with machine
+
+00:23:46.290 --> 00:23:47.940
+learning and computer science.
+
+00:23:48.380 --> 00:23:51.140
+It's much more so far.
+
+00:23:51.140 --> 00:23:52.880
+Machine learning is much more rigid and
+
+00:23:52.880 --> 00:23:55.370
+focused and.
+
+00:23:55.520 --> 00:23:56.850
+And challenging.
+
+00:23:56.850 --> 00:24:00.675
+And so you can think about how did you
+
+00:24:00.675 --> 00:24:02.520
+learn to do things, how do you learn to
+
+00:24:02.520 --> 00:24:03.144
+make predictions?
+
+00:24:03.144 --> 00:24:05.760
+And think about how it may be similar
+
+00:24:05.760 --> 00:24:07.940
+or different from the machine learning
+
+00:24:07.940 --> 00:24:09.480
+algorithms that we learn about.
+
+00:24:12.410 --> 00:24:13.760
+So.
+
+00:24:14.230 --> 00:24:15.800
+All right, so now I'm getting more into
+
+00:24:15.800 --> 00:24:18.030
+the logistics of the course I
+
+00:24:18.030 --> 00:24:19.280
+introduced myself already.
+
+00:24:19.280 --> 00:24:21.100
+I want to introduce a few of the tasks
+
+00:24:21.100 --> 00:24:21.660
+that are here.
+
+00:24:21.660 --> 00:24:23.380
+There's one traveling and one sick.
+
+00:24:24.700 --> 00:24:26.960
+But some of them are here, so one is
+
+00:24:26.960 --> 00:24:29.120
+Vatsal Chheda.
+
+00:24:29.120 --> 00:24:30.650
+Could you introduce yourself?
+
+00:24:32.750 --> 00:24:34.880
+And I think I should probably give you
+
+00:24:34.880 --> 00:24:35.570
+the mic.
+
+00:24:39.030 --> 00:24:40.740
+If I can, sort of.
+
+00:24:46.550 --> 00:24:49.735
+Hello everyone my name is Vatsal, I'm
+
+00:24:49.735 --> 00:24:53.290
+from India and the like the
+
+00:24:53.290 --> 00:24:54.490
+applications of applied machine
+
+00:24:54.490 --> 00:24:55.070
+learning are.
+
+00:24:55.070 --> 00:24:56.390
+I'm using it in neural machine
+
+00:24:56.390 --> 00:24:58.460
+translation from Polish language to
+
+00:24:58.460 --> 00:25:00.630
+English language and in various NLP
+
+00:25:00.630 --> 00:25:01.420
+projects.
+
+00:25:02.740 --> 00:25:03.540
+Thank you.
+
+00:25:05.100 --> 00:25:08.220
+I think Josh is out sick today, Weijie.
+
+00:25:14.790 --> 00:25:16.900
+Hi everyone, I'm Weijie.
+
+00:25:16.900 --> 00:25:19.235
+I'm a second year Masters student and
+
+00:25:19.235 --> 00:25:20.565
+I'm from China.
+
+00:25:20.565 --> 00:25:23.920
+So I basically do research about
+
+00:25:23.920 --> 00:25:26.860
+computer vision and machine learning.
+
+00:25:26.860 --> 00:25:28.510
+So nice to meet you all.
+
+00:25:28.510 --> 00:25:29.120
+Thank you.
+
+00:25:31.120 --> 00:25:32.440
+OK.
+
+00:25:32.440 --> 00:25:34.720
+And since each units here, I'll go to
+
+00:25:34.720 --> 00:25:35.570
+YouTube next.
+
+00:25:39.540 --> 00:25:41.770
+Hi everyone I'm reaching you and I'm a
+
+00:25:41.770 --> 00:25:44.863
+first Masters student in the group and
+
+00:25:44.863 --> 00:25:48.420
+I came from Shenzhen City located in
+
+00:25:48.420 --> 00:25:51.630
+southern China and I'm interested in 3D
+
+00:25:51.630 --> 00:25:53.240
+reconstruction and 3D scene
+
+00:25:53.240 --> 00:25:53.850
+understanding.
+
+00:25:53.850 --> 00:25:56.160
+And during my research I have utilized
+
+00:25:56.160 --> 00:25:59.400
+some deep neural network, RESNET and
+
+00:25:59.400 --> 00:26:01.040
+multi layer perception and they are
+
+00:26:01.040 --> 00:26:04.010
+really useful and can produce pretty
+
+00:26:04.010 --> 00:26:04.910
+decent results.
+
+00:26:04.910 --> 00:26:06.550
+Looking forward to learning with you.
+
+00:26:06.610 --> 00:26:07.150
+Thank you.
+
+00:26:10.220 --> 00:26:12.630
+All right and.
+
+00:26:12.990 --> 00:26:16.420
+OK, **** is traveling and let's see
+
+00:26:16.420 --> 00:26:17.080
+Mington.
+
+00:26:21.990 --> 00:26:24.343
+Hi everyone, my name is Min Hongchang
+
+00:26:24.343 --> 00:26:26.820
+and I'm a first year masters master
+
+00:26:26.820 --> 00:26:28.790
+student in computer science and I'm
+
+00:26:28.790 --> 00:26:33.210
+from China and my current research is
+
+00:26:33.210 --> 00:26:35.380
+focused on computer vision, especially
+
+00:26:35.380 --> 00:26:38.515
+for 3D Vision and generative modeling.
+
+00:26:38.515 --> 00:26:41.690
+And I hope you all enjoyed this course.
+
+00:26:41.690 --> 00:26:42.380
+Thank you.
+
+00:26:43.010 --> 00:26:45.090
+Here and Wentao.
+
+00:26:51.480 --> 00:26:54.630
+So on Wentao and I'm a second year
+
+00:26:54.630 --> 00:26:57.080
+master student here and last semester
+
+00:26:57.080 --> 00:27:01.095
+also tax CS440 if someone take it.
+
+00:27:01.095 --> 00:27:04.510
+And my research mainly focus on any
+
+00:27:04.510 --> 00:27:06.380
+kind of sequential Prediction like
+
+00:27:06.380 --> 00:27:07.560
+natural language understanding,
+
+00:27:07.560 --> 00:27:09.840
+national generation, even some
+
+00:27:09.840 --> 00:27:12.670
+sequential Prediction in India Vision
+
+00:27:12.670 --> 00:27:15.070
+post like trajectory.
+
+00:27:15.130 --> 00:27:17.540
+So if you have any question about that
+
+00:27:17.540 --> 00:27:18.600
+you can ask me.
+
+00:27:21.050 --> 00:27:22.085
+Alright, thank you.
+
+00:27:22.085 --> 00:27:25.510
+And I'll have Josh and kitchenette
+
+00:27:25.510 --> 00:27:27.090
+introduced themselves.
+
+00:27:28.350 --> 00:27:29.690
+When they another time.
+
+00:27:34.360 --> 00:27:34.830
+All right.
+
+00:27:34.830 --> 00:27:36.490
+So there's.
+
+00:27:37.730 --> 00:27:39.526
+So there's three main topics that we're
+
+00:27:39.526 --> 00:27:40.080
+going to cover.
+
+00:27:40.080 --> 00:27:43.090
+One is Supervised Learning
+
+00:27:43.090 --> 00:27:43.850
+Fundamentals.
+
+00:27:43.850 --> 00:27:46.346
+So we'll do the next lecture is going
+
+00:27:46.346 --> 00:27:48.890
+to be on KNN's and then we'll talk
+
+00:27:48.890 --> 00:27:52.160
+about Naive Bayes algorithm and be kind
+
+00:27:52.160 --> 00:27:55.406
+of a review of probability as well.
+
+00:27:55.406 --> 00:27:58.170
+Then linear and logistic regression
+
+00:27:58.170 --> 00:28:01.180
+trees and random forests, and maybe
+
+00:28:01.180 --> 00:28:05.383
+boosting ensemble methods, SVMs, neural
+
+00:28:05.383 --> 00:28:07.070
+networks and deep neural networks.
+
+00:28:07.900 --> 00:28:09.590
+Then the next section, the class, we're
+
+00:28:09.590 --> 00:28:10.880
+going to talk about some different
+
+00:28:10.880 --> 00:28:12.010
+application domains.
+
+00:28:12.910 --> 00:28:15.447
+So we'll talk about computer vision and
+
+00:28:15.447 --> 00:28:19.320
+using CNS for computer vision language
+
+00:28:19.320 --> 00:28:21.770
+Models, will talk about using the
+
+00:28:21.770 --> 00:28:24.424
+transformer networks for vision and
+
+00:28:24.424 --> 00:28:24.930
+language.
+
+00:28:24.930 --> 00:28:26.720
+This idea that you may have heard about
+
+00:28:26.720 --> 00:28:28.200
+called Foundation Models where you
+
+00:28:28.200 --> 00:28:30.789
+where you create machine, where you
+
+00:28:30.790 --> 00:28:32.280
+train machine learning models on a lot
+
+00:28:32.280 --> 00:28:33.855
+of data and then you kind of adapt it
+
+00:28:33.855 --> 00:28:34.565
+for other tasks.
+
+00:28:34.565 --> 00:28:37.070
+And so we'll talk about task and the
+
+00:28:37.070 --> 00:28:41.800
+main adaptation audio as well the
+
+00:28:41.800 --> 00:28:42.500
+ethics.
+
+00:28:42.570 --> 00:28:43.120
+Issues.
+
+00:28:43.120 --> 00:28:45.600
+Really ethical issues relating to
+
+00:28:45.600 --> 00:28:46.310
+machine learning.
+
+00:28:47.040 --> 00:28:48.540
+And data.
+
+00:28:49.460 --> 00:28:50.620
+And then the final section.
+
+00:28:50.620 --> 00:28:53.150
+The class is on parent Discovery and
+
+00:28:53.150 --> 00:28:54.990
+that focuses more on the unsupervised
+
+00:28:54.990 --> 00:28:56.990
+method, so methods for Clustering and
+
+00:28:56.990 --> 00:28:59.290
+retrieval, missing data and the
+
+00:28:59.290 --> 00:29:01.290
+expectation maximization algorithm
+
+00:29:01.290 --> 00:29:02.530
+Density estimation.
+
+00:29:03.590 --> 00:29:06.090
+Creating Topic Models, dealing with
+
+00:29:06.090 --> 00:29:08.710
+outliers and data visualization.
+
+00:29:08.710 --> 00:29:11.390
+CCA, So some of the details might
+
+00:29:11.390 --> 00:29:13.840
+change, especially towards the end of
+
+00:29:13.840 --> 00:29:14.360
+the class.
+
+00:29:14.360 --> 00:29:16.960
+I'm not setting it in stone yet because
+
+00:29:16.960 --> 00:29:20.110
+I'll see how things evolve and see if I
+
+00:29:20.110 --> 00:29:22.690
+have better ideas later, but at least
+
+00:29:22.690 --> 00:29:26.780
+the initial few weeks are more or less
+
+00:29:26.780 --> 00:29:29.090
+determined in the schedules online, as
+
+00:29:29.090 --> 00:29:29.800
+I'll show.
+
+00:29:32.380 --> 00:29:33.170
+So.
+
+00:29:33.260 --> 00:29:33.920
+
+
+00:29:37.340 --> 00:29:37.600
+Don't.
+
+00:29:37.600 --> 00:29:39.410
+I don't usually have people cheer when
+
+00:29:39.410 --> 00:29:41.950
+I show the show the Assignments.
+
+00:29:43.180 --> 00:29:45.690
+Alright, so there's 222 different
+
+00:29:45.690 --> 00:29:46.850
+components to your grades.
+
+00:29:46.850 --> 00:29:48.670
+There's homeworks and Final projects.
+
+00:29:48.670 --> 00:29:50.700
+There's four signed homeworks.
+
+00:29:50.700 --> 00:29:52.640
+Those are worth at least 100 points
+
+00:29:52.640 --> 00:29:53.000
+each.
+
+00:29:53.710 --> 00:29:56.190
+So you can see homework one now that's
+
+00:29:56.190 --> 00:29:59.930
+worth 160 points, but the only about
+
+00:29:59.930 --> 00:30:01.413
+100 is really expected.
+
+00:30:01.413 --> 00:30:03.830
+So for most of the homeworks, there's a
+
+00:30:03.830 --> 00:30:07.762
+core of like 100 points that is that is
+
+00:30:07.762 --> 00:30:10.910
+more prescripted and then there's like
+
+00:30:10.910 --> 00:30:13.770
+additional points for that require more
+
+00:30:13.770 --> 00:30:15.110
+independent exploration.
+
+00:30:16.320 --> 00:30:18.550
+There's a Final project that's worth
+
+00:30:18.550 --> 00:30:21.940
+100 points, and what I'm planning to do
+
+00:30:21.940 --> 00:30:26.270
+for that is to select a few.
+
+00:30:26.380 --> 00:30:29.820
+A few key challenges, for example,
+
+00:30:29.820 --> 00:30:32.335
+something like the Netflix challenge,
+
+00:30:32.335 --> 00:30:34.820
+and I'll solicit your input on that.
+
+00:30:34.820 --> 00:30:36.910
+And so everyone would have the option
+
+00:30:36.910 --> 00:30:38.540
+of doing one of those challenges.
+
+00:30:38.540 --> 00:30:40.660
+Or you can just do your own custom
+
+00:30:40.660 --> 00:30:41.180
+project.
+
+00:30:43.190 --> 00:30:45.150
+So you can kind of.
+
+00:30:47.170 --> 00:30:47.610
+There is.
+
+00:30:47.610 --> 00:30:49.055
+There's a lot of different students in
+
+00:30:49.055 --> 00:30:50.380
+the class from different backgrounds
+
+00:30:50.380 --> 00:30:51.600
+and with different interests.
+
+00:30:51.600 --> 00:30:53.870
+And so rather than trying to tell
+
+00:30:53.870 --> 00:30:56.200
+everybody, make everybody do exactly
+
+00:30:56.200 --> 00:30:57.430
+the same thing.
+
+00:30:57.430 --> 00:31:00.129
+I generally like to have kind of like
+
+00:31:00.130 --> 00:31:02.240
+structure, but options so that if
+
+00:31:02.240 --> 00:31:04.430
+you're not interested in something or
+
+00:31:04.430 --> 00:31:07.449
+if you're Schedule is really bad for a
+
+00:31:07.450 --> 00:31:09.505
+couple weeks, you can.
+
+00:31:09.505 --> 00:31:11.465
+There's flexibility built in so that
+
+00:31:11.465 --> 00:31:12.869
+you can do the things that make the
+
+00:31:12.870 --> 00:31:15.220
+most sense for you so.
+
+00:31:15.380 --> 00:31:16.706
+If you're in the three credit version
+
+00:31:16.706 --> 00:31:18.779
+of the course, you're graded out of a
+
+00:31:18.780 --> 00:31:21.910
+total of 450 points, which means that
+
+00:31:21.910 --> 00:31:24.940
+you need to do 4 1/2 of those things.
+
+00:31:24.940 --> 00:31:25.816
+But you could.
+
+00:31:25.816 --> 00:31:28.835
+You could, for example, do like 3
+
+00:31:28.835 --> 00:31:32.095
+homeworks and do all the extra points
+
+00:31:32.095 --> 00:31:33.440
+that are available, and then you'd
+
+00:31:33.440 --> 00:31:35.130
+probably be done for the semester.
+
+00:31:35.130 --> 00:31:38.386
+Or you could do like the bare minimum
+
+00:31:38.386 --> 00:31:43.479
+on 3 homeworks, do half and half of
+
+00:31:43.480 --> 00:31:45.530
+another homework in the Final project.
+
+00:31:45.590 --> 00:31:47.370
+Or any combination, it's up to you.
+
+00:31:48.540 --> 00:31:50.500
+And for the four credit version, you
+
+00:31:50.500 --> 00:31:54.682
+have to do like everything and a little
+
+00:31:54.682 --> 00:31:55.597
+bit more.
+
+00:31:55.597 --> 00:31:59.670
+So you have to do the four Assignments
+
+00:31:59.670 --> 00:32:02.967
+a little bit of the extra, some of the,
+
+00:32:02.967 --> 00:32:06.730
+some of the stretch goals and the Final
+
+00:32:06.730 --> 00:32:07.280
+project.
+
+00:32:07.280 --> 00:32:09.020
+But again, there's enough opportunity
+
+00:32:09.020 --> 00:32:10.400
+that you could just do the four
+
+00:32:10.400 --> 00:32:12.230
+homeworks and the stretch goals and be
+
+00:32:12.230 --> 00:32:16.073
+done or do the OR do a more basic job.
+
+00:32:16.073 --> 00:32:18.089
+There's a little bit of.
+
+00:32:18.150 --> 00:32:20.730
+Extra credit available so if you do an
+
+00:32:20.730 --> 00:32:21.510
+extra.
+
+00:32:22.400 --> 00:32:24.470
+You can earn an additional 15 points
+
+00:32:24.470 --> 00:32:27.080
+beyond what's beyond the amount needed
+
+00:32:27.080 --> 00:32:29.150
+for 100% of the homework grade.
+
+00:32:29.890 --> 00:32:32.180
+So I have in the Syllabus, I have like
+
+00:32:32.180 --> 00:32:33.870
+the actual equation for computing your
+
+00:32:33.870 --> 00:32:34.120
+grades.
+
+00:32:34.120 --> 00:32:36.310
+So if there's any confusion about how
+
+00:32:36.310 --> 00:32:38.127
+that works, you can look at that
+
+00:32:38.127 --> 00:32:39.486
+equation and it's pretty.
+
+00:32:39.486 --> 00:32:41.130
+I think it's pretty straightforward, at
+
+00:32:41.130 --> 00:32:41.970
+least once you see that.
+
+00:32:43.280 --> 00:32:47.032
+Late policy likewise, I like to be kind
+
+00:32:47.032 --> 00:32:51.537
+of flexible and mainly the main reason
+
+00:32:51.537 --> 00:32:53.620
+to have late penalties is that I want
+
+00:32:53.620 --> 00:32:56.560
+to keep everybody on track and also for
+
+00:32:56.560 --> 00:32:59.950
+course logistics have things so that we
+
+00:32:59.950 --> 00:33:01.359
+can like kind of grade things and be
+
+00:33:01.360 --> 00:33:02.410
+done with them at some point.
+
+00:33:03.770 --> 00:33:06.010
+So the late policy is that you get up
+
+00:33:06.010 --> 00:33:07.730
+to 10 free late days that you can use
+
+00:33:07.730 --> 00:33:09.030
+on any of these Assignments.
+
+00:33:09.790 --> 00:33:12.250
+The exception may be the Final project,
+
+00:33:12.250 --> 00:33:13.930
+since that's due towards at the end of
+
+00:33:13.930 --> 00:33:15.330
+this semester question.
+
+00:33:15.430 --> 00:33:16.840
+Cortana Final project.
+
+00:33:16.840 --> 00:33:19.290
+Our TV is going to sign us like massive
+
+00:33:19.290 --> 00:33:20.070
+kit like.
+
+00:33:27.580 --> 00:33:30.310
+So the question is, for the Final
+
+00:33:30.310 --> 00:33:32.600
+project, will the TAs create a massive
+
+00:33:32.600 --> 00:33:33.690
+GitHub repository?
+
+00:33:34.400 --> 00:33:36.380
+And share it?
+
+00:33:36.380 --> 00:33:37.710
+Or do you create your own?
+
+00:33:38.940 --> 00:33:39.730
+So.
+
+00:33:40.770 --> 00:33:43.070
+Most likely the TAs will not create
+
+00:33:43.070 --> 00:33:44.950
+anything for the Final project.
+
+00:33:44.950 --> 00:33:47.520
+So you would because there many people
+
+00:33:47.520 --> 00:33:49.920
+may do their custom projects and then
+
+00:33:49.920 --> 00:33:52.386
+you're doing you're working with it
+
+00:33:52.386 --> 00:33:54.010
+could be online, you could start with
+
+00:33:54.010 --> 00:33:56.260
+an online repository or do it from
+
+00:33:56.260 --> 00:33:58.150
+scratch and likewise for the
+
+00:33:58.150 --> 00:33:59.040
+challenges.
+
+00:33:59.120 --> 00:34:02.660
+And if we pick Kaggle challenges for
+
+00:34:02.660 --> 00:34:04.440
+example, there's some, there's a little
+
+00:34:04.440 --> 00:34:06.000
+bit of like infrastructure around
+
+00:34:06.000 --> 00:34:08.240
+those, but mostly for the final
+
+00:34:08.240 --> 00:34:10.050
+projects, I want it to be more
+
+00:34:10.050 --> 00:34:10.800
+open-ended.
+
+00:34:10.800 --> 00:34:12.100
+So yeah.
+
+00:34:13.960 --> 00:34:15.690
+And how did this?
+
+00:34:15.690 --> 00:34:17.070
+Yeah.
+
+00:34:19.100 --> 00:34:21.351
+So the Final project can be done in a
+
+00:34:21.351 --> 00:34:23.037
+group or it can be if you so.
+
+00:34:23.037 --> 00:34:25.310
+If you do a custom project it has I
+
+00:34:25.310 --> 00:34:27.145
+want it has to be in a group.
+
+00:34:27.145 --> 00:34:29.060
+If you do one of the challenges you can
+
+00:34:29.060 --> 00:34:30.482
+either do it individually or in a
+
+00:34:30.482 --> 00:34:30.640
+group.
+
+00:34:32.250 --> 00:34:33.380
+Up to four.
+
+00:34:34.740 --> 00:34:35.380
+Yeah.
+
+00:34:37.680 --> 00:34:40.490
+OK, so back to the late policy.
+
+00:34:40.490 --> 00:34:41.690
+So the.
+
+00:34:42.110 --> 00:34:43.835
+So you get up to 10 free late days.
+
+00:34:43.835 --> 00:34:45.460
+You can use them on any combination of
+
+00:34:45.460 --> 00:34:45.678
+projects.
+
+00:34:45.678 --> 00:34:47.400
+You can be 10 days late for one
+
+00:34:47.400 --> 00:34:49.640
+project, you can be 5 days late for two
+
+00:34:49.640 --> 00:34:50.830
+projects, and so on.
+
+00:34:51.810 --> 00:34:54.860
+And then there's a 5 point penalty per
+
+00:34:54.860 --> 00:34:55.900
+day after that.
+
+00:34:56.860 --> 00:34:58.510
+So for example, if you would have
+
+00:34:58.510 --> 00:35:02.422
+earned 110 points, but your five days
+
+00:35:02.422 --> 00:35:03.900
+late and you only had three late days
+
+00:35:03.900 --> 00:35:05.580
+left, then you would earn 100 points
+
+00:35:05.580 --> 00:35:06.090
+instead.
+
+00:35:07.170 --> 00:35:08.920
+And then the project.
+
+00:35:08.920 --> 00:35:11.945
+Every assignment, though, has to every
+
+00:35:11.945 --> 00:35:13.770
+homework has to be submitted within two
+
+00:35:13.770 --> 00:35:15.655
+weeks of the due date to receive any
+
+00:35:15.655 --> 00:35:15.930
+points.
+
+00:35:15.930 --> 00:35:19.114
+So you can't submit homework one like
+
+00:35:19.114 --> 00:35:20.786
+three weeks late and still get points
+
+00:35:20.786 --> 00:35:21.350
+for it.
+
+00:35:21.350 --> 00:35:23.201
+And part of the reason for that is that
+
+00:35:23.201 --> 00:35:25.769
+I don't want, I don't want any students
+
+00:35:25.769 --> 00:35:28.330
+to just get like chronically behind
+
+00:35:28.330 --> 00:35:29.583
+where you're submitting every
+
+00:35:29.583 --> 00:35:31.040
+assignment two or three weeks late,
+
+00:35:31.040 --> 00:35:32.050
+because I've seen that happen.
+
+00:35:32.050 --> 00:35:34.570
+And then I've had students build up
+
+00:35:34.570 --> 00:35:36.480
+like 60 late days by the end of this
+
+00:35:36.480 --> 00:35:37.280
+semester.
+
+00:35:37.930 --> 00:35:40.340
+So it's better just to move on if
+
+00:35:40.340 --> 00:35:41.330
+you're 2 weeks late.
+
+00:35:43.450 --> 00:35:44.460
+
+
+00:35:46.510 --> 00:35:46.900
+Homework.
+
+00:35:48.990 --> 00:35:51.270
+The Final project I have not yet
+
+00:35:51.270 --> 00:35:53.690
+decided, but you definitely can't be
+
+00:35:53.690 --> 00:35:54.875
+the Final project.
+
+00:35:54.875 --> 00:35:57.890
+I might allow one or two late days, but
+
+00:35:57.890 --> 00:35:59.880
+probably not more than that because
+
+00:35:59.880 --> 00:36:02.340
+it's at the it's due on the last day of
+
+00:36:02.340 --> 00:36:04.030
+the semester, so there's not.
+
+00:36:04.030 --> 00:36:05.920
+So there needs to be time for grading
+
+00:36:05.920 --> 00:36:07.940
+and also time for you to do your exams.
+
+00:36:10.780 --> 00:36:13.350
+Alright, so Covid's Masks and sickness,
+
+00:36:13.350 --> 00:36:15.270
+so I do like it.
+
+00:36:15.270 --> 00:36:16.880
+If you come to I think it's a good idea
+
+00:36:16.880 --> 00:36:18.290
+to come to lecture whenever you can.
+
+00:36:18.290 --> 00:36:19.980
+I realize it's early in the morning for
+
+00:36:19.980 --> 00:36:22.770
+many people, but it's probably the best
+
+00:36:22.770 --> 00:36:26.865
+way to keep you on Schedule and even if
+
+00:36:26.865 --> 00:36:28.870
+you just come and.
+
+00:36:29.220 --> 00:36:32.179
+And our, I don't know, doing doing
+
+00:36:32.180 --> 00:36:32.881
+other work or whatever.
+
+00:36:32.881 --> 00:36:34.350
+At least you're at least you're like
+
+00:36:34.350 --> 00:36:35.750
+keeping tabs on what's going on in the
+
+00:36:35.750 --> 00:36:36.410
+course.
+
+00:36:36.970 --> 00:36:41.190
+But everything will be recorded, so
+
+00:36:41.190 --> 00:36:44.090
+it's you're also perfectly capable of
+
+00:36:44.090 --> 00:36:46.040
+catching up on anything that you missed
+
+00:36:46.040 --> 00:36:46.547
+online.
+
+00:36:46.547 --> 00:36:49.720
+So if you're well, do come to lectures
+
+00:36:49.720 --> 00:36:51.820
+and in office hours if you have
+
+00:36:51.820 --> 00:36:52.690
+something to discuss.
+
+00:36:53.610 --> 00:36:55.140
+Master optional.
+
+00:36:55.260 --> 00:36:58.440
+If you're sick, if you're sick, do you
+
+00:36:58.440 --> 00:37:00.120
+stay home because nobody else wants to
+
+00:37:00.120 --> 00:37:00.570
+get sick?
+
+00:37:01.560 --> 00:37:03.860
+You never need to show any proof of
+
+00:37:03.860 --> 00:37:06.910
+illness or you never need to ask me for
+
+00:37:06.910 --> 00:37:08.160
+permission to miss a class.
+
+00:37:08.160 --> 00:37:10.880
+You can just miss it and then you can
+
+00:37:10.880 --> 00:37:12.490
+watch the recording.
+
+00:37:12.490 --> 00:37:16.163
+So the lectures will be recorded so you
+
+00:37:16.163 --> 00:37:17.480
+can always catch up on them.
+
+00:37:17.480 --> 00:37:19.970
+As I was saying also the exams are
+
+00:37:19.970 --> 00:37:22.039
+planned to be on Prairie learn, so you
+
+00:37:22.040 --> 00:37:23.496
+would be able to take them at home.
+
+00:37:23.496 --> 00:37:24.874
+In fact, you won't be able to take them
+
+00:37:24.874 --> 00:37:27.080
+in the lecture, you will need to take
+
+00:37:27.080 --> 00:37:29.970
+them at home and the exams will be open
+
+00:37:29.970 --> 00:37:30.270
+book.
+
+00:37:32.600 --> 00:37:34.910
+Doesn't necessarily mean they're easy.
+
+00:37:36.460 --> 00:37:36.810
+Open.
+
+00:37:44.860 --> 00:37:46.420
+No, it's not.
+
+00:37:46.420 --> 00:37:48.810
+All right, so.
+
+00:37:48.880 --> 00:37:50.960
+For their homeworks, you will implement
+
+00:37:50.960 --> 00:37:52.870
+and apply machine learning methods in
+
+00:37:52.870 --> 00:37:53.810
+Jupiter notebooks.
+
+00:37:55.020 --> 00:37:55.895
+I'll show you.
+
+00:37:55.895 --> 00:37:57.640
+I'll go through the homework on the in
+
+00:37:57.640 --> 00:38:00.450
+the next lecture on Thursday, but
+
+00:38:00.450 --> 00:38:02.510
+you'll see that the basic structure is
+
+00:38:02.510 --> 00:38:04.630
+that you have a.
+
+00:38:05.260 --> 00:38:07.165
+You have like a main assignment page
+
+00:38:07.165 --> 00:38:10.220
+and then there's starter code which is
+
+00:38:10.220 --> 00:38:12.390
+pretty minimal but just enough to give
+
+00:38:12.390 --> 00:38:13.840
+some structure and to load the data
+
+00:38:13.840 --> 00:38:14.460
+that you need.
+
+00:38:15.220 --> 00:38:18.130
+And then there's a.
+
+00:38:18.760 --> 00:38:20.775
+So they're like places where you would
+
+00:38:20.775 --> 00:38:22.280
+where you would write the code for each
+
+00:38:22.280 --> 00:38:24.980
+section, and then there there's a
+
+00:38:24.980 --> 00:38:27.970
+report template which is template for
+
+00:38:27.970 --> 00:38:30.340
+reporting the results of your
+
+00:38:30.340 --> 00:38:31.040
+Algorithms.
+
+00:38:32.160 --> 00:38:34.300
+And then there's also like a tips page
+
+00:38:34.300 --> 00:38:36.660
+which has common code and some other
+
+00:38:36.660 --> 00:38:38.110
+suggestions about the assignment.
+
+00:38:41.270 --> 00:38:43.490
+Alright, so this is the main Learning
+
+00:38:43.490 --> 00:38:45.490
+resource, the website, I mean this is
+
+00:38:45.490 --> 00:38:47.170
+kind of the central repository of
+
+00:38:47.170 --> 00:38:48.230
+everything that you need.
+
+00:38:51.110 --> 00:38:53.190
+So that showed up there.
+
+00:38:53.190 --> 00:38:57.530
+OK, so you can find it from my home
+
+00:38:57.530 --> 00:38:58.030
+page.
+
+00:38:58.030 --> 00:38:59.427
+And also it's the.
+
+00:38:59.427 --> 00:39:01.405
+It's just like the standard location.
+
+00:39:01.405 --> 00:39:04.076
+So if you do like
+
+00:39:04.076 --> 00:39:06.909
+courses.endure.illinois.edu/CS 441.
+
+00:39:07.630 --> 00:39:09.320
+I think even that will go.
+
+00:39:09.320 --> 00:39:11.420
+Yeah, even that will just go to spring
+
+00:39:11.420 --> 00:39:12.090
+2023.
+
+00:39:12.880 --> 00:39:15.895
+So you can see there's a Syllabus.
+
+00:39:15.895 --> 00:39:18.230
+There's obviously no lecture recordings
+
+00:39:18.230 --> 00:39:20.740
+yet, but once they should show up at
+
+00:39:20.740 --> 00:39:23.320
+this location on media space.
+
+00:39:24.770 --> 00:39:26.700
+There's the CampusWire.
+
+00:39:26.700 --> 00:39:28.160
+You can sign up with this code.
+
+00:39:28.160 --> 00:39:30.640
+I'll probably enroll everybody at some
+
+00:39:30.640 --> 00:39:32.575
+point this week that's registered for
+
+00:39:32.575 --> 00:39:33.190
+the class.
+
+00:39:34.350 --> 00:39:38.615
+The submission for Canvas, the
+
+00:39:38.615 --> 00:39:39.250
+Textbook.
+
+00:39:39.250 --> 00:39:42.400
+So I don't really teach out of a
+
+00:39:42.400 --> 00:39:46.160
+textbook, but this book is quite good.
+
+00:39:46.160 --> 00:39:47.620
+Applied machine Learning by David
+
+00:39:47.620 --> 00:39:49.350
+Forsyth, the professor here.
+
+00:39:49.350 --> 00:39:50.916
+He actually created the first version
+
+00:39:50.916 --> 00:39:54.685
+of this course and I do look at the
+
+00:39:54.685 --> 00:39:55.960
+Textbook to make sure I'm not
+
+00:39:55.960 --> 00:39:57.460
+forgetting anything really important.
+
+00:39:58.420 --> 00:40:01.650
+And it's useful to have like another
+
+00:40:01.650 --> 00:40:03.340
+perspective on some of the topics I'm
+
+00:40:03.340 --> 00:40:06.860
+teaching, or to have like more like
+
+00:40:06.860 --> 00:40:07.850
+background reading.
+
+00:40:08.930 --> 00:40:12.560
+So I'll this is the schedule of topics,
+
+00:40:12.560 --> 00:40:15.225
+so you can see that right now we're in
+
+00:40:15.225 --> 00:40:16.300
+the introduction.
+
+00:40:16.300 --> 00:40:18.750
+I've got the PowerPoint slides and the
+
+00:40:18.750 --> 00:40:19.375
+PDF here.
+
+00:40:19.375 --> 00:40:22.490
+I generally try to put them online
+
+00:40:22.490 --> 00:40:24.750
+before I teach, but I can't guarantee
+
+00:40:24.750 --> 00:40:25.250
+it.
+
+00:40:27.280 --> 00:40:29.940
+There is a I've got a bunch of
+
+00:40:29.940 --> 00:40:31.420
+tutorials here.
+
+00:40:31.420 --> 00:40:33.310
+These were originally created for the
+
+00:40:33.310 --> 00:40:35.050
+computational photography course, but
+
+00:40:35.050 --> 00:40:37.680
+they're pretty general, so one is on
+
+00:40:37.680 --> 00:40:40.630
+using Jupiter notebooks, one is on
+
+00:40:40.630 --> 00:40:42.590
+Numpy, and another is on linear
+
+00:40:42.590 --> 00:40:46.150
+algebra, and they're really good, so I
+
+00:40:46.150 --> 00:40:48.420
+would recommend watching those they
+
+00:40:48.420 --> 00:40:48.710
+have.
+
+00:40:48.710 --> 00:40:51.860
+It's a video, but some of them, like
+
+00:40:51.860 --> 00:40:53.770
+this one for Jupiter notebook, also
+
+00:40:53.770 --> 00:40:55.780
+comes with a notebook and they'll like
+
+00:40:55.780 --> 00:40:57.380
+pause and give you time to try things
+
+00:40:57.380 --> 00:40:58.050
+out yourself.
+
+00:40:59.110 --> 00:41:00.540
+So those are worth reviewing,
+
+00:41:00.540 --> 00:41:02.570
+especially if you're not familiar with
+
+00:41:02.570 --> 00:41:04.230
+Jupiter notebooks, if you're not
+
+00:41:04.230 --> 00:41:08.290
+familiar with Python, or if you've feel
+
+00:41:08.290 --> 00:41:09.620
+like you need a refresher on linear
+
+00:41:09.620 --> 00:41:10.130
+algebra.
+
+00:41:11.900 --> 00:41:13.900
+The Assignments are linked to here, so
+
+00:41:13.900 --> 00:41:15.960
+if you click on that you can it should
+
+00:41:15.960 --> 00:41:17.170
+take you to homework one.
+
+00:41:17.890 --> 00:41:21.480
+And you can check that out if you're
+
+00:41:21.480 --> 00:41:22.770
+interested.
+
+00:41:22.770 --> 00:41:24.770
+Like I said, I'm going to review that
+
+00:41:24.770 --> 00:41:26.082
+on Thursday.
+
+00:41:26.082 --> 00:41:30.445
+And that first homework mainly is the
+
+00:41:30.445 --> 00:41:32.450
+that covers the topics that are taught
+
+00:41:32.450 --> 00:41:33.650
+in the first three lectures.
+
+00:41:33.650 --> 00:41:34.830
+KNN maybes.
+
+00:41:35.460 --> 00:41:37.860
+And Linear Logistic Regression.
+
+00:41:39.990 --> 00:41:41.250
+So.
+
+00:41:42.260 --> 00:41:42.630
+Yes.
+
+00:41:42.630 --> 00:41:43.770
+So the other thing.
+
+00:41:45.510 --> 00:41:46.440
+Show you the Syllabus.
+
+00:41:46.440 --> 00:41:47.520
+So here's the Syllabus.
+
+00:41:48.430 --> 00:41:49.755
+Do you read it on your own?
+
+00:41:49.755 --> 00:41:52.660
+You can see more or less the same
+
+00:41:52.660 --> 00:41:55.240
+information that I just gave you, but
+
+00:41:55.240 --> 00:41:57.460
+it has the, I guess a little bit more
+
+00:41:57.460 --> 00:41:58.322
+detail on some things.
+
+00:41:58.322 --> 00:41:59.950
+It has the greeting equations.
+
+00:42:00.880 --> 00:42:03.680
+So the also has grade Thresholds, so.
+
+00:42:04.570 --> 00:42:06.001
+If you reach these graded Thresholds
+
+00:42:06.001 --> 00:42:07.507
+then you're guaranteed that grade.
+
+00:42:07.507 --> 00:42:10.219
+So if you get like a 97 you will
+
+00:42:10.220 --> 00:42:11.480
+definitely get an A plus.
+
+00:42:11.480 --> 00:42:13.940
+I generally will look at the grade
+
+00:42:13.940 --> 00:42:16.560
+distribution at the end of the semester
+
+00:42:16.560 --> 00:42:19.790
+and if it seems warranted then I might
+
+00:42:19.790 --> 00:42:21.710
+lower the Thresholds but I won't raise
+
+00:42:21.710 --> 00:42:22.000
+them.
+
+00:42:22.000 --> 00:42:24.147
+So if you get like a 90, you're
+
+00:42:24.147 --> 00:42:24.953
+guaranteed a minus.
+
+00:42:24.953 --> 00:42:27.209
+It might be that if you get an 89 you
+
+00:42:27.210 --> 00:42:30.444
+could also get an A minus, but I it
+
+00:42:30.444 --> 00:42:31.205
+depends on.
+
+00:42:31.205 --> 00:42:34.110
+It depends on like I have to see.
+
+00:42:34.160 --> 00:42:36.130
+Have to at the end of the semester so I
+
+00:42:36.130 --> 00:42:38.080
+don't curve any individual Assignments
+
+00:42:38.080 --> 00:42:38.500
+I do.
+
+00:42:38.500 --> 00:42:39.820
+I can curve.
+
+00:42:40.770 --> 00:42:43.670
+The final grades, but usually it will
+
+00:42:43.670 --> 00:42:45.480
+not change very much.
+
+00:42:45.480 --> 00:42:47.780
+So just think of these as your targets.
+
+00:42:49.530 --> 00:42:51.320
+Late policy explained.
+
+00:42:52.810 --> 00:42:55.650
+I think all of this, yeah.
+
+00:42:55.650 --> 00:42:59.340
+So I guess it's worth noting that this
+
+00:42:59.340 --> 00:43:01.420
+is the first time I've taught this
+
+00:43:01.420 --> 00:43:04.740
+course, and so I.
+
+00:43:05.490 --> 00:43:08.660
+I'm I wanna keep some flexibility
+
+00:43:08.660 --> 00:43:09.255
+options open.
+
+00:43:09.255 --> 00:43:11.060
+I'm going to be soliciting feedback at
+
+00:43:11.060 --> 00:43:11.850
+various points.
+
+00:43:12.670 --> 00:43:16.480
+I may course correct, so I'll do so in
+
+00:43:16.480 --> 00:43:18.530
+a way that's not disruptive and give
+
+00:43:18.530 --> 00:43:20.320
+you as much notice as possible.
+
+00:43:20.320 --> 00:43:22.050
+But I don't want to set.
+
+00:43:22.050 --> 00:43:23.410
+I don't want to.
+
+00:43:24.670 --> 00:43:26.680
+To be completely inflexible just so
+
+00:43:26.680 --> 00:43:28.820
+that I can make improvements that may
+
+00:43:28.820 --> 00:43:30.110
+benefit your experience.
+
+00:43:32.170 --> 00:43:35.470
+Alright, so yeah, that's enough of
+
+00:43:35.470 --> 00:43:36.182
+reviewing this.
+
+00:43:36.182 --> 00:43:38.120
+So you should read all of this stuff,
+
+00:43:38.120 --> 00:43:40.620
+but I don't need to do it right now.
+
+00:43:42.800 --> 00:43:44.400
+Alright, I think I talked about that
+
+00:43:44.400 --> 00:43:45.310
+stuff.
+
+00:43:45.310 --> 00:43:50.900
+OK, so the office hours, I do have them
+
+00:43:50.900 --> 00:43:53.270
+ready, but I'll create a post on
+
+00:43:53.270 --> 00:43:55.120
+CampusWire that will tell you what the
+
+00:43:55.120 --> 00:43:56.580
+office hours and pin them.
+
+00:43:56.580 --> 00:43:58.340
+The office hours will start next week.
+
+00:43:59.390 --> 00:44:01.380
+And then, like I said, the reading.
+
+00:44:01.380 --> 00:44:03.010
+The main Readings are the applied
+
+00:44:03.010 --> 00:44:05.260
+machine learning book by David Forsyth.
+
+00:44:09.660 --> 00:44:12.370
+Alright, so Academic Integrity.
+
+00:44:12.470 --> 00:44:13.190
+
+
+00:44:14.040 --> 00:44:15.860
+It's OK to discuss your homework with
+
+00:44:15.860 --> 00:44:16.750
+classmates.
+
+00:44:16.750 --> 00:44:20.780
+And actually, if you and somebody else
+
+00:44:20.780 --> 00:44:23.100
+finish your homework, I would actually
+
+00:44:23.100 --> 00:44:26.585
+encourage you to like to like, discuss
+
+00:44:26.585 --> 00:44:28.304
+like what were your results?
+
+00:44:28.304 --> 00:44:30.520
+And if your results don't match, then
+
+00:44:30.520 --> 00:44:31.880
+find out what they did.
+
+00:44:31.880 --> 00:44:34.570
+I'm OK with that, but if you haven't
+
+00:44:34.570 --> 00:44:36.330
+done it yet, don't like look at their
+
+00:44:36.330 --> 00:44:39.130
+code so that you can do the exact same
+
+00:44:39.130 --> 00:44:40.316
+thing that they did.
+
+00:44:40.316 --> 00:44:41.990
+So I think, like, there's actually a
+
+00:44:41.990 --> 00:44:43.850
+lot of educational value in learning
+
+00:44:43.850 --> 00:44:45.180
+from other students and.
+
+00:44:45.890 --> 00:44:47.392
+Sometimes there's going to be more than
+
+00:44:47.392 --> 00:44:49.470
+one way to implement something, and one
+
+00:44:49.470 --> 00:44:50.840
+way will lead to better results than
+
+00:44:50.840 --> 00:44:51.300
+another.
+
+00:44:51.950 --> 00:44:55.280
+And it's worth finding out what that
+
+00:44:55.280 --> 00:44:56.537
+better way is.
+
+00:44:56.537 --> 00:44:59.980
+But you should mainly do things
+
+00:44:59.980 --> 00:45:04.070
+independently for the Assignments.
+
+00:45:04.070 --> 00:45:05.710
+Like I said, for the Final project you
+
+00:45:05.710 --> 00:45:07.300
+can work in groups, but for the regular
+
+00:45:07.300 --> 00:45:08.570
+Assignments it should be mostly
+
+00:45:08.570 --> 00:45:09.310
+independent work.
+
+00:45:09.950 --> 00:45:12.000
+And any consultation with others is
+
+00:45:12.000 --> 00:45:14.220
+should be aimed at further enhancing
+
+00:45:14.220 --> 00:45:16.700
+your what you learn rather than
+
+00:45:16.700 --> 00:45:17.800
+bypassing it.
+
+00:45:19.730 --> 00:45:21.322
+You can look at Stack Overflow.
+
+00:45:21.322 --> 00:45:23.036
+You can get ideas from online.
+
+00:45:23.036 --> 00:45:25.700
+At the end of the template there's a
+
+00:45:25.700 --> 00:45:27.940
+section where you say what other
+
+00:45:27.940 --> 00:45:29.340
+sources you have.
+
+00:45:29.340 --> 00:45:32.031
+And so if you talk to anybody about the
+
+00:45:32.031 --> 00:45:33.234
+course, I mean not the course.
+
+00:45:33.234 --> 00:45:34.439
+If you talk to anybody about the
+
+00:45:34.440 --> 00:45:36.132
+assignment or looked at Stack Overflow
+
+00:45:36.132 --> 00:45:38.410
+or whatever, you should just list those
+
+00:45:38.410 --> 00:45:38.960
+things there.
+
+00:45:38.960 --> 00:45:39.280
+So.
+
+00:45:40.070 --> 00:45:42.600
+As you're doing the assignment if you.
+
+00:45:42.810 --> 00:45:44.140
+If you have like.
+
+00:45:45.780 --> 00:45:48.190
+If you end up using other resources,
+
+00:45:48.190 --> 00:45:49.840
+just write them down South that you
+
+00:45:49.840 --> 00:45:50.870
+remember to put them there.
+
+00:45:52.500 --> 00:45:55.470
+So you shouldn't like copy any code
+
+00:45:55.470 --> 00:45:56.570
+from anybody.
+
+00:45:56.570 --> 00:45:58.530
+So you should never be like claiming
+
+00:45:58.530 --> 00:46:00.250
+credit for something that somebody else
+
+00:46:00.250 --> 00:46:01.510
+wrote, even if you typed it out
+
+00:46:01.510 --> 00:46:02.880
+yourself.
+
+00:46:02.880 --> 00:46:05.050
+And you should not use external
+
+00:46:05.050 --> 00:46:06.620
+resources but without acknowledging
+
+00:46:06.620 --> 00:46:06.840
+them.
+
+00:46:07.640 --> 00:46:08.950
+So if you're not sure if something's
+
+00:46:08.950 --> 00:46:11.900
+OK, you can just ask and as long as you
+
+00:46:11.900 --> 00:46:14.279
+acknowledge all your sources then you
+
+00:46:14.279 --> 00:46:16.230
+can then you're safe.
+
+00:46:16.230 --> 00:46:16.830
+So.
+
+00:46:17.610 --> 00:46:19.865
+Even if even if what you did is
+
+00:46:19.865 --> 00:46:22.685
+something that I would consider to be
+
+00:46:22.685 --> 00:46:25.524
+like 2 too much copying or something,
+
+00:46:25.524 --> 00:46:27.290
+if you just if you disclose it and
+
+00:46:27.290 --> 00:46:28.410
+you're acknowledgements, it's not
+
+00:46:28.410 --> 00:46:32.031
+cheating, it's just not would not be
+
+00:46:32.031 --> 00:46:33.840
+like doing enough I guess.
+
+00:46:36.840 --> 00:46:38.110
+So.
+
+00:46:39.080 --> 00:46:39.840
+Alright.
+
+00:46:39.840 --> 00:46:42.800
+So yeah, in terms of Prerequisites, so
+
+00:46:42.800 --> 00:46:44.380
+I'm going to assume that you've had
+
+00:46:44.380 --> 00:46:46.140
+some kind of probability and statistics
+
+00:46:46.140 --> 00:46:46.990
+before.
+
+00:46:46.990 --> 00:46:49.240
+I will review it a little bit when I
+
+00:46:49.240 --> 00:46:52.790
+talk about Naive Bayes, but it's really
+
+00:46:52.790 --> 00:46:56.060
+like a 20 or 30 minute review to what
+
+00:46:56.060 --> 00:46:57.990
+would normally be a course.
+
+00:46:57.990 --> 00:47:01.019
+So it's not meant to replace replace
+
+00:47:01.020 --> 00:47:03.130
+having taken probably probability or
+
+00:47:03.130 --> 00:47:04.905
+statistics and that's not supposed to
+
+00:47:04.905 --> 00:47:07.890
+be stages, it's stats I think.
+
+00:47:08.460 --> 00:47:11.610
+And the linear algebra.
+
+00:47:11.610 --> 00:47:13.410
+So again, I'm going to assume that you
+
+00:47:13.410 --> 00:47:15.830
+things like matrix multiplication, what
+
+00:47:15.830 --> 00:47:18.070
+inverses, stuff like that.
+
+00:47:19.280 --> 00:47:23.030
+If not, if not, then you should
+
+00:47:23.030 --> 00:47:26.010
+probably learn it first, but you can
+
+00:47:26.010 --> 00:47:27.590
+review it using the tutorial.
+
+00:47:28.450 --> 00:47:30.930
+And then also assume some knowledge of
+
+00:47:30.930 --> 00:47:33.230
+calculus, like if I take a derivative,
+
+00:47:33.230 --> 00:47:33.620
+what is?
+
+00:47:33.620 --> 00:47:35.280
+And you kind of know you how to take
+
+00:47:35.280 --> 00:47:36.480
+derivatives yourself.
+
+00:47:37.530 --> 00:47:40.490
+And if you have experience with Python,
+
+00:47:40.490 --> 00:47:41.990
+that will help, because we'll be doing
+
+00:47:41.990 --> 00:47:44.440
+the assignments in Python, but it's not
+
+00:47:44.440 --> 00:47:46.920
+necessary, I mean I myself.
+
+00:47:48.780 --> 00:47:49.820
+Don't.
+
+00:47:49.820 --> 00:47:52.490
+I've used Python a bit.
+
+00:47:52.560 --> 00:47:55.750
+And I find it pretty easy to do the
+
+00:47:55.750 --> 00:47:58.590
+coding, but I'm not like an expert in
+
+00:47:58.590 --> 00:48:00.404
+Python by any means, so it's not.
+
+00:48:00.404 --> 00:48:02.016
+It's not extremely challenging coding
+
+00:48:02.016 --> 00:48:03.257
+in my opinion.
+
+00:48:03.257 --> 00:48:06.823
+So if you're generally capable of
+
+00:48:06.823 --> 00:48:08.170
+coding, then you're capable of picking
+
+00:48:08.170 --> 00:48:09.890
+up Python And doing it, but if you
+
+00:48:09.890 --> 00:48:10.810
+already know, it will help.
+
+00:48:12.290 --> 00:48:14.090
+And then like I said, if watch the
+
+00:48:14.090 --> 00:48:16.820
+tutorials if you would like to review
+
+00:48:16.820 --> 00:48:17.740
+those concepts.
+
+00:48:20.990 --> 00:48:24.290
+So I also want to just briefly mention
+
+00:48:24.290 --> 00:48:25.840
+how this course is different from some
+
+00:48:25.840 --> 00:48:27.280
+others, because that's a really common
+
+00:48:27.280 --> 00:48:28.465
+question.
+
+00:48:28.465 --> 00:48:32.320
+So one of the other main courses is
+
+00:48:32.320 --> 00:48:34.610
+446, which is just called machine
+
+00:48:34.610 --> 00:48:35.120
+learning.
+
+00:48:36.080 --> 00:48:38.490
+So this course, when I say this course
+
+00:48:38.490 --> 00:48:40.180
+here, I mean this one that we're in
+
+00:48:40.180 --> 00:48:43.078
+right now, 441 this course provides a
+
+00:48:43.078 --> 00:48:45.160
+foundation for ML practice, while I
+
+00:48:45.160 --> 00:48:47.370
+would say 446 provides more of a
+
+00:48:47.370 --> 00:48:48.700
+foundation for machine learning
+
+00:48:48.700 --> 00:48:49.630
+research.
+
+00:48:49.630 --> 00:48:52.710
+And so compared to 446, we will have
+
+00:48:52.710 --> 00:48:55.660
+less theory, fewer derivations, less
+
+00:48:55.660 --> 00:48:59.440
+focus on optimization and more focus on
+
+00:48:59.440 --> 00:49:01.610
+how you represent things on data
+
+00:49:01.610 --> 00:49:03.300
+representations and developing
+
+00:49:03.300 --> 00:49:05.510
+applications and.
+
+00:49:05.570 --> 00:49:07.140
+Application examples.
+
+00:49:07.140 --> 00:49:08.620
+So it doesn't mean that we don't have
+
+00:49:08.620 --> 00:49:10.630
+any theory, but it's all everything in
+
+00:49:10.630 --> 00:49:12.790
+the course is geared towards making you
+
+00:49:12.790 --> 00:49:15.860
+a good machine learning practitioner
+
+00:49:15.860 --> 00:49:19.035
+rather than making you a good machine
+
+00:49:19.035 --> 00:49:20.340
+learning researcher.
+
+00:49:20.340 --> 00:49:22.260
+There are just different focuses.
+
+00:49:23.090 --> 00:49:24.580
+Also, if you look at the syllabus for
+
+00:49:24.580 --> 00:49:27.090
+446, you'll see that there are some
+
+00:49:27.090 --> 00:49:28.770
+topics that are in common and then
+
+00:49:28.770 --> 00:49:30.540
+there's others that are different so.
+
+00:49:31.660 --> 00:49:33.740
+But you can just, like look at doing an
+
+00:49:33.740 --> 00:49:36.490
+AB comparison on the syllabi once if
+
+00:49:36.490 --> 00:49:38.390
+the 446 Syllabus is available.
+
+00:49:38.390 --> 00:49:40.700
+There's also an online version of this
+
+00:49:40.700 --> 00:49:42.640
+course that's currently taught by Marco
+
+00:49:42.640 --> 00:49:43.160
+Morales.
+
+00:49:45.060 --> 00:49:48.220
+This is a complete redesign for this
+
+00:49:48.220 --> 00:49:48.610
+semester.
+
+00:49:48.610 --> 00:49:52.500
+My version is a total redesign, so they
+
+00:49:52.500 --> 00:49:54.680
+cover similar topics but in different
+
+00:49:54.680 --> 00:49:55.170
+ways.
+
+00:49:57.340 --> 00:50:00.820
+Assignment wise, this course has fewer
+
+00:50:00.820 --> 00:50:02.700
+and larger homeworks that are a little
+
+00:50:02.700 --> 00:50:04.550
+bit more open-ended.
+
+00:50:06.450 --> 00:50:08.780
+And also as a Final project and exams,
+
+00:50:08.780 --> 00:50:10.820
+while the online version, at least the
+
+00:50:10.820 --> 00:50:13.780
+last one I saw has like 11 homeworks
+
+00:50:13.780 --> 00:50:16.210
+that are a little bit more scripted and
+
+00:50:16.210 --> 00:50:20.100
+it has quizzes and it's just a.
+
+00:50:21.320 --> 00:50:23.050
+Very different in the kind of
+
+00:50:23.050 --> 00:50:23.500
+structure.
+
+00:50:25.420 --> 00:50:26.090
+And.
+
+00:50:27.300 --> 00:50:29.720
+And I would say that compared to the
+
+00:50:29.720 --> 00:50:31.660
+online one, the one that I'm teaching
+
+00:50:31.660 --> 00:50:33.870
+now focuses more on a conceptual
+
+00:50:33.870 --> 00:50:37.300
+understanding of the techniques and on
+
+00:50:37.300 --> 00:50:38.850
+how machine learning is used today.
+
+00:50:40.730 --> 00:50:43.440
+And then one more example is there's a
+
+00:50:43.440 --> 00:50:45.005
+deep Learning course for computer
+
+00:50:45.005 --> 00:50:45.710
+vision.
+
+00:50:45.710 --> 00:50:48.566
+So that's focused on, as it says, deep
+
+00:50:48.566 --> 00:50:50.400
+learning and computer vision, where
+
+00:50:50.400 --> 00:50:52.916
+this course is not only focused on deep
+
+00:50:52.916 --> 00:50:54.850
+learning, I teach a variety of
+
+00:50:54.850 --> 00:50:56.955
+different techniques, including deep
+
+00:50:56.955 --> 00:50:58.090
+learning to some extent.
+
+00:50:58.770 --> 00:51:01.850
+And also talk about a broader variety
+
+00:51:01.850 --> 00:51:02.890
+of Application Domains.
+
+00:51:04.570 --> 00:51:06.482
+So you may be wondering whether you
+
+00:51:06.482 --> 00:51:07.310
+take the course.
+
+00:51:07.310 --> 00:51:09.029
+I would say you should take the course
+
+00:51:09.030 --> 00:51:10.150
+if you want to learn how to apply
+
+00:51:10.150 --> 00:51:10.760
+machine learning.
+
+00:51:11.810 --> 00:51:13.626
+If you like coding based homeworks and
+
+00:51:13.626 --> 00:51:15.280
+you're also OK with math, then you
+
+00:51:15.280 --> 00:51:16.863
+might want to take the course.
+
+00:51:16.863 --> 00:51:20.210
+Also, you need to be willing to spend a
+
+00:51:20.210 --> 00:51:21.930
+significant amount of time, so I would
+
+00:51:21.930 --> 00:51:23.700
+say it would probably take 10 to 12
+
+00:51:23.700 --> 00:51:24.760
+hours per week.
+
+00:51:24.760 --> 00:51:28.103
+If you don't know if you're catching up
+
+00:51:28.103 --> 00:51:29.610
+on a lot of things then it could take
+
+00:51:29.610 --> 00:51:31.806
+even longer or depending on how fast of
+
+00:51:31.806 --> 00:51:33.290
+a programmer you are and things like
+
+00:51:33.290 --> 00:51:33.840
+that.
+
+00:51:33.840 --> 00:51:37.470
+But this is my estimate, so it is a
+
+00:51:37.470 --> 00:51:38.450
+it's not a light course.
+
+00:51:40.230 --> 00:51:42.640
+Don't take the course if so.
+
+00:51:42.640 --> 00:51:43.790
+If you want a more theoretical
+
+00:51:43.790 --> 00:51:46.070
+background, 446 would be more
+
+00:51:46.070 --> 00:51:47.680
+specifically for you.
+
+00:51:47.680 --> 00:51:49.590
+I think there could be value in taking
+
+00:51:49.590 --> 00:51:50.300
+both of them.
+
+00:51:50.300 --> 00:51:52.630
+When was I took like a big variety of
+
+00:51:52.630 --> 00:51:55.000
+machine learning courses and I never
+
+00:51:55.000 --> 00:51:56.400
+really minded if they had overlap
+
+00:51:56.400 --> 00:51:57.770
+because I always found it useful to
+
+00:51:57.770 --> 00:51:59.650
+revisit a topic and to get different
+
+00:51:59.650 --> 00:52:00.990
+perspectives on it.
+
+00:52:00.990 --> 00:52:03.460
+But 446 is more theory oriented.
+
+00:52:04.610 --> 00:52:06.770
+And if you want to focus on a single
+
+00:52:06.770 --> 00:52:08.460
+Application Domain, again there's more
+
+00:52:08.460 --> 00:52:10.680
+focused courses for that, like a Vision
+
+00:52:10.680 --> 00:52:13.020
+or NLP or special topics course.
+
+00:52:13.680 --> 00:52:15.650
+And if you want an easy A, this is also
+
+00:52:15.650 --> 00:52:18.160
+probably not the right course.
+
+00:52:18.160 --> 00:52:20.022
+It is possible for everybody to get in
+
+00:52:20.022 --> 00:52:21.790
+a if you were to.
+
+00:52:22.180 --> 00:52:23.970
+To do well on the Assignments and
+
+00:52:23.970 --> 00:52:26.750
+exams, but it's definitely not going to
+
+00:52:26.750 --> 00:52:27.210
+be easy.
+
+00:52:29.920 --> 00:52:32.090
+And as I mentioned earlier, your
+
+00:52:32.090 --> 00:52:33.240
+feedback is welcome.
+
+00:52:33.240 --> 00:52:35.715
+So I will occasionally solicit feedback
+
+00:52:35.715 --> 00:52:37.590
+and if you respond, that would help me.
+
+00:52:38.580 --> 00:52:40.005
+You can always talk to me after class
+
+00:52:40.005 --> 00:52:41.860
+or send me a message on CampusWire or
+
+00:52:41.860 --> 00:52:43.530
+come to my office hours.
+
+00:52:43.630 --> 00:52:47.530
+And my philosophy in teaching is to be
+
+00:52:47.530 --> 00:52:48.715
+a force multiplier.
+
+00:52:48.715 --> 00:52:51.328
+So for every I want every hour of
+
+00:52:51.328 --> 00:52:53.039
+effort that you put into the course to
+
+00:52:53.040 --> 00:52:55.150
+produce as much learning as possible.
+
+00:52:55.150 --> 00:52:58.090
+So it means that you do need to put an
+
+00:52:58.090 --> 00:53:00.419
+effort to get value out of the course,
+
+00:53:00.420 --> 00:53:03.040
+but I try to select the topics and
+
+00:53:03.040 --> 00:53:03.800
+Assignments.
+
+00:53:04.920 --> 00:53:07.420
+And organize the materials in a way
+
+00:53:07.420 --> 00:53:09.450
+that makes your Learning efficient.
+
+00:53:11.860 --> 00:53:13.710
+Alright, so now what do you do next?
+
+00:53:13.710 --> 00:53:16.120
+Bookmark the website so that you can go
+
+00:53:16.120 --> 00:53:17.600
+back to it easily?
+
+00:53:17.600 --> 00:53:19.160
+Sign up for CampusWire?
+
+00:53:19.880 --> 00:53:22.300
+Read the syllabus and the schedule and
+
+00:53:22.300 --> 00:53:24.330
+I would recommend watching the
+
+00:53:24.330 --> 00:53:25.000
+tutorials.
+
+00:53:25.760 --> 00:53:27.650
+And then in the next class I'm going to
+
+00:53:27.650 --> 00:53:30.570
+talk about K nearest neighbor and kind
+
+00:53:30.570 --> 00:53:32.740
+of an overview of classification and
+
+00:53:32.740 --> 00:53:33.710
+regression.
+
+00:53:33.710 --> 00:53:35.843
+And I'll also introduce homework 1 S
+
+00:53:35.843 --> 00:53:37.400
+you can check out homework one if you
+
+00:53:37.400 --> 00:53:40.059
+want, or just wait till the next class
+
+00:53:40.060 --> 00:53:41.110
+when I introduce it.
+
+00:53:42.990 --> 00:53:45.040
+Alright, so that's it for today.
+
+00:53:45.040 --> 00:53:48.420
+I'll first before you get up, does
+
+00:53:48.420 --> 00:53:50.000
+anybody have any questions that you
+
+00:53:50.000 --> 00:53:51.420
+want to ask?
+
+00:53:51.420 --> 00:53:54.460
+Wait, don't get up, don't pick up your
+
+00:53:54.460 --> 00:53:55.120
+things yet.
+
+00:53:55.120 --> 00:53:55.960
+Yes.
+
+00:53:56.240 --> 00:53:56.680
+Join the.
+
+00:53:58.240 --> 00:54:00.240
+OK.
+
+00:54:00.240 --> 00:54:01.000
+Anything else?
+
+00:54:01.780 --> 00:54:03.560
+Alright, so just come up to me if you
+
+00:54:03.560 --> 00:54:05.730
+have any questions and I'll be here for
+
+00:54:05.730 --> 00:54:06.070
+a while.
+
+00:54:07.630 --> 00:54:08.600
+See you Thursday.
+
+00:54:10.050 --> 00:54:12.390
+A limited submission on the exams.
+
+00:54:12.390 --> 00:54:15.270
+What do you have like?
+
+01:14:27.910 --> 01:14:29.830
+Testing, testing, testing.
+
+01:14:40.680 --> 01:14:41.020
+OK.
+
+01:14:43.690 --> 01:14:44.490
+Every time you come.
+

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