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2. Given a set of basic scientific problems, the student will construct simple programmes to investigate the problems. |
3. Given a scientific problem, the student will identify and evaluate the outputs through appropriate visualisation. |
4. Given a scientific problem, the student will discriminate and select basic approaches to scientific computing, including the use of cloud services. |
5. Given an appropriate data set, the student will write a program to process the data e.g. find the principal components. |
6. Given an image, the student will write a program to implement an operation on the image e.g. dithering to reduce its bit depth. |
7. Given a classifier, the student will write a program to implement and analyse it e.g. plot its decision boundary; display an animation of its trajectory of weights over the error surface. |
Affective (Attitudes and Values) |
1. Given datasets, the student will question whether the data is representative and attempt to address any biases. |
2. Given problems to investigate, the student will identify and discuss any potential ethical considerations. |
3. On completion of an investigation using appropriate outputs including visualisation, the student will be able to defend the approach adopted. |
Psychomotor (Physical Skills) |
N/A |
How the Module will be Taught and what will be the Learning Experiences of the Students: |
The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. |
Research Findings Incorporated in to the Syllabus (If Relevant): |
Prime Texts: |
Langtangen (2016) A Primer on Scientific Programming with Python, Springer |
Other Texts: |
Beazley (2016) Python Essential Reference, 4th ed., O'Reilly |
Flach (2012) Machine Learning, Cambridge |
Goodfellow & Bengio (2014) Deep Learning, MIT Press |
Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press |
Foster & Gannon (2017) Cloud Computing for Science and Engineering, MIT Press |
Programmes |
Semester(s) Module is Offered: |
Autumn |
Module Leader: |
pepijn.vandeven@ul.ie |
________________ |
Module Code - Title: |
CE4031 - INTRODUCTION TO DEEP LEARNING AND FRAMEWORKS |
Year Last Offered: |
2024/5 |
Hours Per Week |
Lecture |
Lab |
Tutorial |
Other |
Private |
Credits |
2 |
0 |
1 |
3 |
4 |
6 |
Grading Type: |
N |
Prerequisite Modules: |
Rationale and Purpose of the Module: |
To give students an insight into Deep Learning and associated Frameworks and prepare them to take more advanced Artificial Intelligence modules. |
Syllabus: |
1. Fundamentals and basic concepts of deep learning and related machine learning |
2. Programming basics for deep learning |
3. Introduction to deep learning frameworks (e.g. TensorFlow, PyTorch, Caffe2, CNTK etc. ) |
4. Deep learning platforms and acceleration |
5. Applications of deep learning (e.g. image classification, signal processing, natural language processing etc) |
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