text stringlengths 3 8.33k | repo stringclasses 52
values | path stringlengths 6 141 | language stringclasses 35
values | sha stringlengths 64 64 | chunk_index int32 0 273 | n_tokens int32 1 896 |
|---|---|---|---|---|---|---|
", "monospace")
.text((d) => `${d.pct.toFixed(1)}%`);
if (!animated) {
labels
.attr("x", PAD.left + 4)
.attr("opacity", 0)
.transition()
.duration(400)
.delay((_d, i) => 800 + i * 50)
.attr("x", (d) => xScale(d.pct) + 4)
.attr("opacity", 1);
}... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-class-frequency.tsx | TypeScript | ba6a2dd7cdf2a5818942e06aa2d6b03d60605c02102d14f97274869d592f25fa | 1 | 209 |
"use client";
import { useRef, useEffect, useState, useCallback } from "react";
import * as d3 from "d3";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
const CELL = 30;
export function D3CorrelationHeatmap() {
const svgRef = useRef<SVGSVGElement>(null);
const [animated, setAnimated] = useState(false);
c... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-correlation-heatmap.tsx | TypeScript | e4efdbcebea3cef24ffd94336f9f2ddc835f17e7fe09a05688ef1588391e01f8 | 0 | 896 |
+ k * CELL + CELL / 2}
y={pad.top - 8}
textAnchor="end"
transform={`rotate(-42 ${pad.left + k * CELL + CELL / 2} ${pad.top - 8})`}
style={{
fill: "hsl(var(--muted-foreground))",
fontSize: "10px",
}}
>
{lab}
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-correlation-heatmap.tsx | TypeScript | 1964c123d4f5d6598d2e88d8c39b5b1d888d89c7f1116bc7e4c49c96250c5525 | 1 | 323 |
"use client";
import { useRef, useEffect, useState, useCallback, useMemo } from "react";
import * as d3 from "d3";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
const W = 520;
const H = 340;
const PAD = { top: 10, right: 40, bottom: 30, left: 150 };
export function D3DeviceChart() {
const svgRef = useRef<SV... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-device-chart.tsx | TypeScript | 8c65eaa227e5636355a5e73a295d4daf5c3c1a6cad25bfa2106e523ae15f9115 | 0 | 896 |
", (d) => yScale(d.name)! + yScale.bandwidth() / 2 + 3)
.style("fill", "hsl(var(--foreground))")
.style("font-size", "9px")
.style("font-family", "monospace")
.text((d) => d.count.toLocaleString());
if (!animated) {
labels
.attr("x", PAD.left + 4)
.attr("opacity", 0)
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-device-chart.tsx | TypeScript | 6f08eca11236ba16740c184542d8c1ba132539ac342fee23e9d8c27c537b8d63 | 1 | 267 |
"use client";
import { useRef, useEffect, useState, useCallback } from "react";
import * as d3 from "d3";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
import { Button } from "@/components/ui/button";
const W = 460;
const H = 260;
const PAD = { top: 30, right: 20, bottom: 40, left: 100 };
export function D3Fe... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-feature-scale.tsx | TypeScript | 1f932e2d79dcb5d035f03f24e41d0b3df523fa90eaa7b656eae0b431278b65f9 | 0 | 896 |
var(--chart-2))")
.attr("opacity", 0);
bars
.transition()
.duration(800)
.ease(d3.easeCubicInOut)
.attr("opacity", 0.7);
}
const means = svg
.selectAll(".mean-mark")
.data(values)
.enter()
.append("line")
.attr("y1", (d) => yScale(d.f... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-feature-scale.tsx | TypeScript | d2e6a56102dfa645162fcefa4d2b1d8af78a0af7c85c49b2f7a7338fb570ce5c | 1 | 589 |
"use client";
import { useRef, useEffect, useState, useCallback, useMemo } from "react";
import * as d3 from "d3";
const SET_A: number[] = [0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0];
const SET_B: number[] = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0];
export function D3IouAnimation() {
const svgRef = useRef<SVGSVGElement>(null... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-iou-animation.tsx | TypeScript | 9e2050408b5b1700fb6511d171066964903558729a55e9c1eace9334492a34da | 0 | 896 |
transition()
.duration(300)
.delay((_d, i) => i * 40)
.attr("opacity", (d) => (d ? 0.8 : 0.2));
const segsB = svg
.selectAll(".seg-b")
.data(SET_B)
.enter()
.append("rect")
.attr("class", "seg-b")
.attr("x", (_d, i) => baseX + i * (segW + gap))
.attr("y",... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-iou-animation.tsx | TypeScript | 0e9dbfb7e59551226eda5266efcbb871b74439b32708d61b8c0d73e3769e48e6 | 1 | 896 |
, iou, w]);
useEffect(() => {
drawScene();
}, [drawScene]);
return (
<div className="space-y-2">
<svg ref={svgRef} width={w} height={h} className="max-w-full" />
<div className="flex gap-2">
{["Show annotations", "Highlight intersection", "Compute IoU"].map(
(label, i) => (... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-iou-animation.tsx | TypeScript | fb6b5e432c84a62f843d1e73d99b1382081f6046f9f39da09aff97a96d408cd5 | 2 | 211 |
"use client";
import { useRef, useEffect, useState, useMemo, useCallback } from "react";
import * as d3 from "d3";
import { mlpcDeckData, CLASS_COLOR } from "@/lib/mlpc-deck-data";
export function D3TsneScatter({ className }: { className?: string }) {
const svgRef = useRef<SVGSVGElement>(null);
const [animated, s... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-tsne-scatter.tsx | TypeScript | 346536bdaaef19c223a254880e2412a1c5f14bc5dda98c189bd37a545e2f8f6c | 0 | 896 |
tsne-point")
.transition()
.duration(300)
.attr("opacity", (d) =>
activeClass === null || d.class === activeClass ? 0.8 : 0.08,
)
.attr("r", (d) =>
activeClass === null ? 3 : d.class === activeClass ? 4 : 2,
);
}, [activeClass]);
return (
<div className={clas... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/d3-tsne-scatter.tsx | TypeScript | 8999d039b396e38c94a5294819a6c29070bb1d3aed21190729466bc11c7f2b26 | 1 | 297 |
"use client";
import "mafs/core.css";
import {
Coordinates,
Mafs,
Polyline,
Text,
Theme,
useMovablePoint,
} from "mafs";
import { AnimatedMetric } from "@/components/deck/animated-metric";
import { BlockMath } from "@/components/deck/math";
import { Card } from "@/components/ui/card";
function intervalIoU... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/iou-interval-mafs.tsx | TypeScript | c115d895a8b00305be84d62264e1997af961931da2a635df12e96936d6314174 | 0 | 860 |
"use client";
import { motion } from "framer-motion";
import { useMemo, useState } from "react";
import { BlockMath } from "@/components/deck/math";
import { Button } from "@/components/ui/button";
import { Card } from "@/components/ui/card";
import { cn } from "@/lib/utils";
const initVotes = [1, 0, 1] as const;
ex... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/majority-vote-scene.tsx | TypeScript | b7a51bdbc237074c09992f5303d2767ed06ca7a88bf1f69b9d81d0abbffc82f2 | 0 | 896 |
0 }}
className="font-mono text-lg"
>
{mean.toFixed(3)}
</motion.div>
</div>
<div className="rounded-md border bg-background/80 p-2">
<div className="text-muted-foreground">Aggregated label</div>
<motion.div
key... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/majority-vote-scene.tsx | TypeScript | 79937ef99187e6f6f3e55e94a2922a2f9a66a36efb266faa3ba42a65032bf7e8 | 1 | 138 |
"use client";
import "katex/dist/katex.min.css";
import { BlockMath as KaTeXBlock, InlineMath as KaTeXInline } from "react-katex";
import { cn } from "@/lib/utils";
export function InlineMath({
math,
className,
}: {
math: string;
className?: string;
}) {
return (
<span className={cn("inline-block align-... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/math.tsx | TypeScript | d4124932eb1f949a846af63279f2ca3fa516fa003fcf0d74bc527581dcdbeab5 | 0 | 185 |
/**
* Single source of truth for slide order (mirrors `mlpc_report.tex` narrative).
* Keys must match exports in `components/pitch/slides/index.tsx`.
*/
export const MLPC_SLIDE_KEYS = [
"SlideMlpcTitle",
"SlideMlpcOverview",
"SlideMlpcFailureModes",
"SlideMlpcCaseStudy",
"SlideMlpcAgreement",
"SlideMlpcI... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/outline.ts | TypeScript | 6f96cf1862f911687371564445b9ec7193d9fb51d6bdcc5d875aef8586a32b83 | 0 | 513 |
"use client";
import { useMemo, useState } from "react";
import { useDrag } from "@use-gesture/react";
import { mlpcDeckData, CLASS_COLOR } from "@/lib/mlpc-deck-data";
import { cn } from "@/lib/utils";
export function TsneScatter({ className }: { className?: string }) {
const points = mlpcDeckData.tsne.points;
c... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/deck/tsne-scatter.tsx | TypeScript | 0de4da264bca979a233273f180a1614e468b72c84aeaef3566fc368d40455b65 | 0 | 698 |
import { SlideMlpcTitle } from "@/components/pitch/slides/mlpc-title";
import { SlideMlpcOverview } from "@/components/pitch/slides/mlpc-overview";
import { SlideMlpcFailureModes } from "@/components/pitch/slides/mlpc-failure-modes";
import { SlideMlpcCaseStudy } from "@/components/pitch/slides/mlpc-case-study";
import... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/index.tsx | TypeScript | 70a6b8732018c4dc95602866fc0c0b99d921ed17e8da196fdcaaa6e25e27d123 | 0 | 353 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { MajorityVoteScene } from "@/components/deck/majority-vote-scene";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
export function SlideMlpcAggregation() {
const share = mlpcDeckData.aggregation?.singleAnnotatorShare ?? 0.17;
ret... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-aggregation.tsx | TypeScript | 0ad531300241dff46786f020dfd3f5833da10f582da8b1b8608886e0b7f5f946 | 0 | 193 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
import { D3AgreementChart } from "@/components/deck/d3-agreement-chart";
export function SlideMlpcAgreement() {
return (
<SlideShell title="Annotator agreement (IoU)" className="col-span-3... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-agreement.tsx | TypeScript | 125cd818449377c4539602d336da5020f15ad17b5decda2e783d314f74ddab7b | 0 | 341 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
import { motion } from "framer-motion";
import { useState } from "react";
import { cn } from "@/lib/utils";
import {
Table,
TableBody,
TableCell,
TableHead,
TableHeader,
TableRow,
} f... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-case-study.tsx | TypeScript | d8e588c2f6a464cac6a9de29d950d4f4edecf1e096181fdc477e68618be3f200 | 0 | 881 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { motion } from "framer-motion";
const biases = [
"Hardware: iPhone-heavy long tail — mic and noise-floor shift on unseen devices.",
"Environment: kitchens ~26 % — kitchen-associated classes over-represented.",
"Annotator: own-recordi... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-conclusions.tsx | TypeScript | ec88b4978100b7f8185035c13511ad4a6ea25603ac9290774a49fd47a0dbb893 | 0 | 598 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { D3CorrelationHeatmap } from "@/components/deck/d3-correlation-heatmap";
export function SlideMlpcCorrTsne() {
return (
<SlideShell title="Feature correlations" className="col-span-3">
<div className="col-span-3 mx-auto flex w-... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-corr-tsne.tsx | TypeScript | 777267288e3d0adba52cbefaec8d70e855a2107d8223248f2c3a285c5fe26313 | 0 | 205 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { motion } from "framer-motion";
const modes = [
{
title: "Systematic omission",
desc: "Annotators missed over half of events in some clips, biasing classifiers toward precision over recall.",
},
{
title: "Acoustic confusi... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-failure-modes.tsx | TypeScript | 6ef2ccb7cf125112bfb8870511a9bcf9d615d5b778258d2a59284939768a4cc2 | 0 | 553 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { D3FeatureScale } from "@/components/deck/d3-feature-scale";
export function SlideMlpcFeatures() {
return (
<SlideShell title="Feature scales" className="col-span-3">
<div className="col-span-3 mx-auto flex w-full max-w-3xl fle... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-features.tsx | TypeScript | 9a84b2743c603113fe8cf1c41c3362c580e707973fe2f504fa3f5a0c051f6c50 | 0 | 163 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { siteConfig } from "@/config/site";
import { motion } from "framer-motion";
const benefits = [
"Assistive listening for hearing-impaired users",
"Elderly care monitoring — fall detection, medication reminders",
"Energy-efficient smar... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-impact.tsx | TypeScript | f8cc869cace3fcb5f5e8cb6644ef4d62f6832696fff479b147ff9726582d6f8f | 0 | 688 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { IouIntervalMafs } from "@/components/deck/iou-interval-mafs";
export function SlideMlpcIouMafs() {
return (
<SlideShell title="IoU — intersection over union" className="col-span-3">
<div className="col-span-3 mx-auto w-full ma... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-iou-mafs.tsx | TypeScript | 6e4d0879eb869527d668dedd4a666c3e8a9fa3a50c761e187d1b3c5c18e10574 | 0 | 107 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { D3ClassFrequency } from "@/components/deck/d3-class-frequency";
export function SlideMlpcLabels() {
return (
<SlideShell title="Label space" className="col-span-3">
<div className="col-span-3 mx-auto flex w-full max-w-3xl flex... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-labels.tsx | TypeScript | 091efdd43c95a28e5e1fbd961127976fa0d2f47072bc98807d5b89728a8dffad | 0 | 167 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
import { D3DeviceChart } from "@/components/deck/d3-device-chart";
export function SlideMlpcMetadata() {
const m = mlpcDeckData.metadata;
return (
<SlideShell title="Recording metadata"... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-metadata.tsx | TypeScript | e42bf47e062cecd9b3aa59b4e13c93c39c383bb301207874438d2aa000f40df0 | 0 | 315 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { motion } from "framer-motion";
const items = [
"Verified annotations in Label Studio — spectrogram playback, side-by-side annotator comparison.",
"Applied boundary rules (±1 s tolerance) to accept, fix, or reject each labeled region."... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-overview.tsx | TypeScript | 99317fae289080d472f09b0fc899f2b829fb23faa9674110f451f5ba27c61430 | 0 | 379 |
"use client";
import SlideShell from "@/components/pitch/slide-shell";
import { motion } from "framer-motion";
import { siteConfig } from "@/config/site";
import { mlpcDeckData } from "@/lib/mlpc-deck-data";
const stats = [
{ value: () => mlpcDeckData.agreement.totalRecordings.toLocaleString(), label: "recordings" ... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/pitch/slides/mlpc-title.tsx | TypeScript | 72f5ee9a5845a6e498d4bbb584d6af18248b33eece7417e30c0f376274fb7341 | 0 | 707 |
"use client";
import * as React from "react";
import * as RechartsPrimitive from "recharts";
import { cn } from "@/lib/utils";
/**
* Minimal shadcn-style chart shell: wraps Recharts `ResponsiveContainer`
* with theme-friendly defaults (full chart styling lives in slide components).
*/
export function ChartContaine... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/components/ui/chart.tsx | TypeScript | 71562fc619af294ce3a0ffdbb60ffe58ee0ea7e44def52afca12d9f196f50064 | 0 | 559 |
export const SLIDE_URI = "https://github.com/julian-at/mlpc-2026-task3";
export const SLIDE_URI_TITLE = "mlpc-2026-task3";
| mlpc-2026-task3 | slides-template/interiorly-pitch-master/config/pitch.ts | TypeScript | 52b5e06a25f1b839153cb28b1642992064b3b336757ceda234fadaf10e375779 | 0 | 36 |
export const siteConfig = {
name: "MLPC 2026 Task 3",
url: "https://mlpc.julianschmidt.cv/",
ogImage: "https://mlpc.julianschmidt.cv/images/og.png",
description:
"Interactive slide deck — MLPC 2026 Task 3 data exploration (SED, annotations, features).",
links: {
github: "https://github.com/julian-at/m... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/config/site.ts | TypeScript | e3c15f46e2405f851b8f03b9a9d3ef146fb6cc75717ac97d23345b47792e5be0 | 0 | 108 |
{
"source": "static_tex_fallback",
"disclaimer": "Co-occurrence, correlation off-diagonal detail, and t-SNE are schematic when dataset is absent; run export with MLPC2026_dataset_development for exact plots.",
"aggregation": {
"thresholdSweep": [
{
"threshold": 0.4,
"deltaOverallIou": 0.... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | e942d71186b8d9cdfd560717358900e59371dfd955564556abfedcdc43853505 | 0 | 896 |
,
"pct": 6.348111912220116
},
{
"class": "vacuum_cleaner",
"count": 16950,
"pct": 10.074952894394285
},
{
"class": "wardrobe_drawer_open_close",
"count": 4250,
"pct": 2.5261681298628735
},
{
"class": "window_open_close",... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 2c053c1889f8f6ef5a21f5f333e8db70fcbede959e4cb9b1ad0f086fa2c223ee | 1 | 896 |
": 585
},
{
"label": "living_room",
"count": 424
},
{
"label": "other / long tail",
"count": 1685
}
],
"uniqueDevices": 369,
"medianDurationSec": 22.5,
"iqrSec": [
18.5,
27.5
],
"durationRangeSec": [
12.0,
39.5... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | ff8dd578fe1bf0fdcf0960824d1e120625fcc07ad9476ed95a15016f10128f23 | 2 | 896 |
0.35
],
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
-0.25,
0.0,
0.0,
0.0,
1.0,
0.0
],
[
0.0,
0.0,
0.0,
0.0,
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 98a5bdf8c724a1ecce2a00c1511eb5565d8ba58dca034dd51786dc6e371befc8 | 3 | 896 |
: 1,
"class": "coffee_machine"
},
{
"x": -3.737,
"y": 1.511,
"classIndex": 1,
"class": "coffee_machine"
},
{
"x": -2.308,
"y": 3.78,
"classIndex": 1,
"class": "coffee_machine"
},
{
"x": -2.545,
"y... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | a46c8af9af750e959f95a0015a91ed77bfc96aa5488ab9e016dc6243cdfaec50 | 4 | 896 |
": -1.389,
"y": -2.605,
"classIndex": 2,
"class": "cutlery_dishes"
},
{
"x": -2.69,
"y": -2.003,
"classIndex": 2,
"class": "cutlery_dishes"
},
{
"x": -0.103,
"y": -3.161,
"classIndex": 2,
"class": "cutlery_di... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | adea53447fc4d69b87137f924822789f424f750ffc426cebff9ac7a210842a1a | 5 | 896 |
,
"y": 0.428,
"classIndex": 3,
"class": "door_open_close"
},
{
"x": 2.654,
"y": -0.825,
"classIndex": 3,
"class": "door_open_close"
},
{
"x": 4.054,
"y": -2.167,
"classIndex": 3,
"class": "door_open_close"
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | ccb8a881e15dd10ef3d1e4887517c9d8a5e95d2a9b160357988b87546a382d69 | 6 | 896 |
},
{
"x": 1.86,
"y": 0.698,
"classIndex": 4,
"class": "footsteps"
},
{
"x": -2.935,
"y": -0.053,
"classIndex": 4,
"class": "footsteps"
},
{
"x": 2.097,
"y": 3.19,
"classIndex": 4,
"class": "foot... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 63c869ac01247147904609e3f8fb046d9a46f0b6cc794478cff5d1129a914d3d | 7 | 896 |
class": "keyboard_typing"
},
{
"x": 4.636,
"y": 5.591,
"classIndex": 5,
"class": "keyboard_typing"
},
{
"x": 3.484,
"y": 6.109,
"classIndex": 5,
"class": "keyboard_typing"
},
{
"x": 5.175,
"y": 5.746,
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | a69814f30f90cb85d3ab49fe377e628276a4fd5c49bfe8f962a44ee77bc2b297 | 8 | 896 |
: 0.759,
"classIndex": 6,
"class": "keychain"
},
{
"x": 2.166,
"y": 2.175,
"classIndex": 6,
"class": "keychain"
},
{
"x": -0.699,
"y": 5.172,
"classIndex": 6,
"class": "keychain"
},
{
"x": 2.425,
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | d9c978c1edfb43f7cfb49c71fe9844d14391ab8beb3ec68db448d05afe0069cd | 9 | 896 |
: "light_switch"
},
{
"x": 0.333,
"y": 3.444,
"classIndex": 7,
"class": "light_switch"
},
{
"x": -2.318,
"y": 3.649,
"classIndex": 7,
"class": "light_switch"
},
{
"x": 1.813,
"y": 1.948,
"classInd... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 47ed515b688c3499eb100548b6469077c6257fed5dd2938d85bd4ff26c66e084 | 10 | 896 |
"
},
{
"x": 3.552,
"y": -4.458,
"classIndex": 8,
"class": "microwave"
},
{
"x": 4.58,
"y": -3.693,
"classIndex": 8,
"class": "microwave"
},
{
"x": 3.904,
"y": -3.304,
"classIndex": 8,
"cla... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | b02d9ef7baca3373f607111d88ade55192b0f93c21b6bd5e013d799b646e39b6 | 11 | 896 |
: 5.994,
"y": 1.413,
"classIndex": 9,
"class": "phone_ringing"
},
{
"x": 7.236,
"y": 1.151,
"classIndex": 9,
"class": "phone_ringing"
},
{
"x": 6.928,
"y": 0.999,
"classIndex": 9,
"class": "phone_ringing"
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 2667f30643715bdd5f41661994ea52f9f52abc81bc3d516455fd5e4b1a7394cd | 12 | 896 |
"y": -6.549,
"classIndex": 10,
"class": "running_water"
},
{
"x": -6.137,
"y": -3.918,
"classIndex": 10,
"class": "running_water"
},
{
"x": -6.67,
"y": -5.164,
"classIndex": 10,
"class": "running_water"
},
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | cad186e468021ce8b53c873a6ff8ef30f11733127ebc25a393e797dcc9111294 | 13 | 896 |
{
"x": -2.358,
"y": -5.865,
"classIndex": 11,
"class": "toilet_flushing"
},
{
"x": 0.025,
"y": -6.152,
"classIndex": 11,
"class": "toilet_flushing"
},
{
"x": -1.081,
"y": -5.617,
"classIndex": 11,
"cl... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | fa726a499394545da82488b2a2033b6c330f08c5af29157b50b76c940ab7e3bf | 14 | 896 |
,
"y": 4.009,
"classIndex": 12,
"class": "vacuum_cleaner"
},
{
"x": -8.345,
"y": 3.769,
"classIndex": 12,
"class": "vacuum_cleaner"
},
{
"x": -7.909,
"y": 4.778,
"classIndex": 12,
"class": "vacuum_cleaner"
... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 7f6c7cabcf520298957bcce751054ae8dc0aceeb5a2a3401a96b610860bdff89 | 15 | 896 |
},
{
"x": 2.039,
"y": 0.754,
"classIndex": 13,
"class": "wardrobe_drawer_open_close"
},
{
"x": 2.255,
"y": 2.043,
"classIndex": 13,
"class": "wardrobe_drawer_open_close"
},
{
"x": 3.639,
"y": -2.514,
"c... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | f8192b22f316158e568475c8ab6fb477b1dc99dea40ffa860fb5dc6ddf2ba610 | 16 | 896 |
: "window_open_close"
},
{
"x": 1.793,
"y": -2.011,
"classIndex": 14,
"class": "window_open_close"
},
{
"x": -1.785,
"y": -5.301,
"classIndex": 14,
"class": "window_open_close"
},
{
"x": 1.321,
"y": -2.95... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/data/mlpc-deck-data.json | JSON | 8cf83bc78fb62d993dbdc225765668d80d7f48c42a0b18725d28490200d1a408 | 17 | 591 |
/** Short labels aligned with `scripts/utils.py` SHORT_CLASS_NAMES */
export const SHORT_CLASS_LABELS = [
"bell",
"coffee",
"cutlery",
"door",
"foot",
"keyboard",
"keychain",
"light",
"micro",
"phone",
"water",
"toilet",
"vacuum",
"wardrobe",
"window",
] as const;
| mlpc-2026-task3 | slides-template/interiorly-pitch-master/lib/class-labels.ts | TypeScript | 82f87fdd1fb95d69f20b04c890ea2e1294efbd9e3f6086a8de681737dc06f4b4 | 0 | 86 |
import raw from "@/data/mlpc-deck-data.json";
export type MlpcDeckData = {
source: string;
disclaimer?: string | null;
aggregation?: {
thresholdSweep: { threshold: number; deltaOverallIou: number }[];
singleAnnotatorShare: number;
};
caseStudy?: {
file: string;
agreementPct: number;
rows:... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/lib/mlpc-deck-data.ts | TypeScript | 310e1de199e25a865a7773d18da898682de570817bdee621d6317a4043602959 | 0 | 573 |
"use client";
import { useCallback } from "react";
import { parseAsInteger, useQueryState } from "nuqs";
import { MLPC_SLIDE_COUNT } from "@/components/deck/outline";
export function useCurrentSlide() {
const [currentSlide, _setCurrentSlide] = useQueryState(
"slide",
parseAsInteger.withOptions({ shallow: tr... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/lib/hooks/use-current-slide.ts | TypeScript | 3a96dbb80cfa1cddca36a20906af0022bd2a16d22cffb38d4fe7072488bfa70d | 0 | 144 |
@tailwind base;
@tailwind components;
@tailwind utilities;
@layer base {
:root {
--background: 0 0% 100%;
--muted: 0 0% 98%;
--background-subtle: 0 0% 98%;
--foreground: 240 10% 15%;
--muted-foreground: 240 3.8% 46.1%;
--card: 0 0% 100%;
--card-foreground: 240 10% 3.9%;
--popover: 0 0... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/styles/globals.css | CSS | a3d693cd042b419191e2087a61cadb5ffdc1d1e790feb2166668e08877258250 | 0 | 779 |
declare module "react-katex" {
import * as React from "react";
export interface MathComponentProps {
math: string;
errorColor?: string;
renderError?: (error: Error) => React.ReactNode;
}
export class InlineMath extends React.Component<MathComponentProps> {}
export class BlockMath extends React.Com... | mlpc-2026-task3 | slides-template/interiorly-pitch-master/types/index.d.ts | TypeScript | d4fc7c2dca169ef6e7822238d7c8caa3251c7aa9a5fda929683c2c3cd0e50137 | 0 | 152 |
# Next.js template
This is a Next.js template with shadcn/ui.
## Adding components
To add components to your app, run the following command:
```bash
npx shadcn@latest add button
```
This will place the ui components in the `components` directory.
## Using components
To use the components in your app, import them... | mlpc-exam | README.md | Markdown | 2d62022e1b9491c2fca8e106cae4970cfc407ea09afbd0cb60c95de0d2dc61e1 | 0 | 98 |
import type { MetadataRoute } from "next"
export default function manifest(): MetadataRoute.Manifest {
return {
name: "MLPC Exam Trainer",
short_name: "MLPC",
description: "Closed-book MLPC exam practice with local progress.",
start_url: "/",
scope: "/",
display: "standalone",
background_... | mlpc-exam | app/manifest.ts | TypeScript | 0641cee32edf8c2a3e3c2519db6b708935f11414a525f534522f55ecc5acee63 | 0 | 164 |
-foreground">·</span> Single
Choice
</h1>
<div className="rounded-md border border-border bg-card px-2.5 py-1.5 font-mono text-xs font-bold shadow-sm">
{examQuestions.length}
</div>
</header>
<div className="mt-10 sm:mt-11">
<MenuLa... | mlpc-exam | components/practice-app.tsx | TypeScript | ecf863a8eaa457126dfbdb7cd899c3480f61d2dddf79d57ba01261fffe26db16 | 5 | 896 |
px]">
<button
className="h-12 w-full rounded-[10px] bg-foreground text-base font-bold text-background shadow-sm transition enabled:hover:bg-foreground/90 disabled:cursor-not-allowed disabled:bg-muted disabled:text-muted-foreground sm:h-[50px]"
disabled={disabled}
onClick={onClick}
... | mlpc-exam | components/practice-app.tsx | TypeScript | 372326420a85c2ea6d876d791444d7ac5c317ce7237b7dca150c77b6091c230a | 11 | 896 |
"Unanswered"
}
return getChoiceText(question, selectedChoiceIds)
}
function getCorrectAnswerText(question: Question) {
return getChoiceText(question, question.correctChoiceIds ?? [])
}
function getChoiceText(question: Question, choiceIds: string[]) {
const choiceIdSet = new Set(choiceIds)
const choices = (... | mlpc-exam | components/practice-app.tsx | TypeScript | d95d7ccdc7bef80084e223588ed48654d2985a47cc87d4fd25fe20b702b59e9f | 12 | 671 |
import { describe, expect, test } from "bun:test"
import { normalizeMathText } from "./math-format"
describe("math text normalization", () => {
test("turns copied loss matrices into LaTeX display math", () => {
expect(normalizeMathText("λ = [[0, 100], [1, 0]]")).toBe(
"$$\\lambda = \\begin{pmatrix}0 & 100... | mlpc-exam | lib/math-format.test.ts | TypeScript | 462b0ba97d0bcb062139e822f4f8092d1e0ca9469dae1924ddad47a6080298ac | 0 | 167 |
export function normalizeMathText(text: string) {
if (text.includes("$")) {
return text
}
return text
.replace(
/λ\s*=\s*\[\[(\d+),\s*(\d+)\],\s*\[(\d+),\s*(\d+)\]\]/g,
(_, a, b, c, d) =>
`$$\\lambda = \\begin{pmatrix}${a} & ${b} \\\\ ${c} & ${d}\\end{pmatrix}$$`
)
.replace(/Ω... | mlpc-exam | lib/math-format.ts | TypeScript | 09ba01f0feae172f60218cdf729c4092624d0cdd67619650daa3f7538a065f03 | 0 | 312 |
import { describe, expect, test } from "bun:test"
import {
EXAM_QUESTIONS,
createDrillSession,
createDefaultProgress,
createMockExamSession,
evaluateChoiceAnswer,
getDrillRemainingQuestionIds,
getDisplayChoices,
getMockExamStats,
getMockReviewItems,
getPracticeQuestions,
getPracticeStats,
updat... | mlpc-exam | lib/practice.test.ts | TypeScript | 6f05d218fb1f6797ce284a86615c74022b90139348db3900b7d63fff0df54235 | 0 | 896 |
([question.id])
const firstCorrect = updateDrillSession(session, question.id, true)
expect(firstCorrect.correctStreaks[question.id]).toBe(1)
expect(getDrillRemainingQuestionIds(firstCorrect)).toEqual([question.id])
const missed = updateDrillSession(firstCorrect, question.id, false)
expect(missed.c... | mlpc-exam | lib/practice.test.ts | TypeScript | 1be10c9b1bf6915fb774786bcfd82cc3a34e8ee1fe370b4c5f42aed531e61f1e | 1 | 395 |
import { questionBank } from "@/lib/data"
import type {
Choice,
Question,
QuestionOrigin,
Source,
Topic,
} from "@/lib/data"
export type PracticeMode = "exam" | "practice" | "review" | "mock" | "drill"
export type PracticeSourceFilter =
| "all"
| "closed-book"
| "approved-old"
| "generated"
export... | mlpc-exam | lib/practice.ts | TypeScript | 252a829be8b3e19b024ead1fa3adc88f8f3b9a15c294c21bd383cf878d982a2d | 0 | 896 |
export function getTopicStats(
questions: Question[],
progress: PracticeProgress
): TopicPracticeStats[] {
return questionBank.topics
.map((topic) => {
const topicQuestions = questions.filter((question) =>
question.topicIds.includes(topic.id)
)
return {
topicId: topic.id,
... | mlpc-exam | lib/practice.ts | TypeScript | fcfca661fa5eb30ef55b7dc5bc16384595d068c6e0a944202528d70a989dac82 | 3 | 896 |
return typeof value === "string" && validQuestionIds.has(value) ? value : null
}
function matchesTopic(question: Question, topicId: "all" | string) {
return topicId === "all" || question.topicIds.includes(topicId)
}
function matchesSourceFilter(
question: Question,
sourceFilter: PracticeSourceFilter
) {
switc... | mlpc-exam | lib/practice.ts | TypeScript | a55ed211ddf9c570384aaa2830995ac477a624b92aef8a901af86617662475c4 | 5 | 532 |
{
"version": "1.0.0",
"generatedAt": "2026-06-18T11:21:53.777447+00:00",
"course": {
"code": "MLPC",
"title": "Machine Learning and Pattern Classification",
"university": "Johannes Kepler University Linz",
"language": "en"
},
"defaults": {
"choicesPerMultipleChoice": 4,
"generatedQuest... | mlpc-exam | lib/data/question-bank.json | JSON | a29778edd19a328a3be5d6c20d9d1cc75528d2c8879a56a7de9b6af08373165c | 0 | 896 |
retake-2.pdf",
"pageCount": 3
},
{
"id": "exam-retake-3",
"kind": "past_exam",
"title": "MLPC retake question export 3",
"fileName": "mlpc-retake-3.pdf",
"pageCount": 2
},
{
"id": "exam-retake-4",
"kind": "past_exam",
"title": "MLPC retake question e... | mlpc-exam | lib/data/question-bank.json | JSON | 09b2b03d4d9cf7f21396a65e6d3a4df2adfa69f7e628b97c110976e0d54e976b | 1 | 896 |
": 1,
"year": 2026,
"pageNumber": 5,
"sourceQuality": "primary_capture"
},
{
"id": "img-closed-book-2026-p06",
"kind": "exam_image",
"title": "2026 closed-book exam image, Group B page 6",
"fileName": "new_img/WhatsApp_Image_2026-06-15_at_13.37.39.jpeg",
"assetPat... | mlpc-exam | lib/data/question-bank.json | JSON | 7d60ad4da6f9a122ac8c4d511a56dbc2fe5012fd2e4282555d203cc4b1fe30a3 | 2 | 896 |
",
"assetPath": "/sources/new_img/closed-book-2026-group-b-page-11.jpeg",
"pageCount": 1,
"year": 2026,
"pageNumber": 11,
"sourceQuality": "primary_capture"
},
{
"id": "generated-study-bank",
"kind": "generated",
"title": "Generated exam-style study questions base... | mlpc-exam | lib/data/question-bank.json | JSON | ad58286efc28b106e759deccc8599a6534b5d7334ebc5954beb7bfa921a6923d | 3 | 896 |
the Forward algorithm."
}
],
"questions": [
{
"id": "img-q001",
"origin": "real_closed_book_exam",
"sourceRefs": [
{
"sourceId": "img-closed-book-2026-p02",
"pages": [
2
],
"questionNumber": "1"
},
{
"sou... | mlpc-exam | lib/data/question-bank.json | JSON | c5e1af9c25e13a14c8f0fb4cdca7f6275294225033ed8b0fbae9837e8213dd9f | 4 | 896 |
"lecture-04",
"pages": [
54
],
"note": "Automatically linked lecture slide by keyword overlap."
},
{
"sourceId": "lecture-04",
"pages": [
52
],
"note": "Automatically linked lecture slide by keyword overlap."
... | mlpc-exam | lib/data/question-bank.json | JSON | facd788b14c48d998f88d905484284238b0d79c40e489930e752c9156330c651 | 5 | 896 |
[
"lecture-04"
],
"difficulty": "easy",
"cognitiveLevel": "calculation",
"interactionType": "single_choice",
"stem": "In country A, 10% of people have disease X. T1 always predicts true, T2 always predicts false, T3 predicts true with probability 50%, and T4 predicts true with prob... | mlpc-exam | lib/data/question-bank.json | JSON | aa8a732314f5fbac45ee25ece42b55fbc7a476f1c79d2ffd202487400ba909ea | 6 | 896 |
precision is undefined rather than a percentage.",
"tags": [
"precision",
"undefined"
],
"retrievalText": "In country A, 10% of people have disease X. T1 always predicts true, T2 always predicts false, T3 predicts true with probability 50%, and T4 predicts true with probability 10%, in... | mlpc-exam | lib/data/question-bank.json | JSON | b4739e7df1500f6323559efb91e4c049b25dca7e3c93281925dbab378e423e5f | 7 | 896 |
[1, 0]] With equal error costs, always predicting false has expected error 10%, lower than T4's 18%, T3's 50%, and T1's 90%. cost-matrix risk bayesian-classification classifier-evaluation",
"groupId": "img-cost-decisions",
"choices": [
{
"id": "c1",
"text": "T1"
},
... | mlpc-exam | lib/data/question-bank.json | JSON | 99926c889c7c3be433b0668d6f1ccb3272936c65d660895346211680d64b09fd | 8 | 896 |
correctAnswerText": "T2",
"examMode": "closed_book",
"sourcePriority": "primary",
"approvalStatus": "approved",
"selectionRationale": "Directly transcribed from the 2026 closed-book exam image and solved against the lecture theory.",
"examStyleConfidence": 0.99,
"commonTrap": "Ignori... | mlpc-exam | lib/data/question-bank.json | JSON | 243d443489c4396bd448874c43171bb31c638971a5355131d838ecfd28519720 | 9 | 896 |
lecture slide by keyword overlap."
},
{
"sourceId": "lecture-04",
"pages": [
50
],
"note": "Automatically linked lecture slide by keyword overlap."
},
{
"sourceId": "lecture-02",
"pages": [
36
]... | mlpc-exam | lib/data/question-bank.json | JSON | c298235011c9e7af56f5267652c7fe45221817ed4c12d77ca245fbd891fbbc56 | 10 | 896 |
fixed class distribution, identical TPR and FPR imply identical accuracy.",
"tags": [
"roc",
"accuracy"
],
"retrievalText": "For a fixed dataset, two classifiers can occupy the same position in ROC space but have different accuracies. For a fixed class distribution, identical TPR and F... | mlpc-exam | lib/data/question-bank.json | JSON | 1103821e142ce3e408238550964cf63bea8235cc858fc0f2191e6fd108a3b5ca | 11 | 896 |
": "lecture-04",
"pages": [
37
],
"note": "Automatically linked lecture slide by keyword overlap."
}
],
"topicIds": [
"classifier-evaluation"
],
"lectureIds": [
"lecture-04"
],
"difficulty": "medium",
"cognitiveL... | mlpc-exam | lib/data/question-bank.json | JSON | 408586b3679bf730aecbf58018b49b1669aff2277c824d59c3dc7d930f51e491 | 12 | 896 |
from the 2026 closed-book exam image and solved against the lecture theory.",
"examStyleConfidence": 0.97,
"commonTrap": "Confusing the requested metric with a nearby metric such as accuracy, precision, recall, or base rate.",
"coreClosedBookLikely": true
},
{
"id": "img-q013",
"or... | mlpc-exam | lib/data/question-bank.json | JSON | 760d49816f42b345203ea3d9f62f2078b0d16eec1736e341352353f54e082dab | 13 | 896 |
"retrievalText": "In a world where false positives and false negatives have the same misclassification costs, recall = accuracy. Equal costs relate to error rate/accuracy, not to recall, which ignores true negatives. recall accuracy cost classifier-evaluation",
"groupId": "img-classification-general",
"choi... | mlpc-exam | lib/data/question-bank.json | JSON | 3837915e8014211566cd91494fa4754911454c18d7e2fbc78e94086aef0b7dfe | 14 | 896 |
[
13
],
"note": "Automatically linked lecture slide by keyword overlap."
}
],
"topicIds": [
"common-classifiers"
],
"lectureIds": [
"lecture-05"
],
"difficulty": "medium",
"cognitiveLevel": "conceptual",
"interaction... | mlpc-exam | lib/data/question-bank.json | JSON | 2a7c2b9ac105187640be9014ef9ff371cd6df3fff42d51e152d9d3cd49a5e26e | 15 | 896 |
": "Directly transcribed from the 2026 closed-book exam image and solved against the lecture theory.",
"examStyleConfidence": 0.97,
"commonTrap": "Ignoring priors or the loss matrix and choosing the intuitively best-looking classifier.",
"coreClosedBookLikely": true
},
{
"id": "img-q018"... | mlpc-exam | lib/data/question-bank.json | JSON | bc25f29b6673ec3ce8e817954c4e6311e3b75956c4c2267ee96a5bc91883d0ff | 16 | 896 |
the number of hidden units in a feed-forward neural network tends to reduce its ability to overfit data. More hidden units increase capacity and can increase overfitting risk. hidden-units capacity neural-networks",
"groupId": "img-overfitting",
"choices": [
{
"id": "true",
"text... | mlpc-exam | lib/data/question-bank.json | JSON | 002a7a983d66d7d9142ecd95a9111f5d6a1a920199b0f841bfa0edfdd8b63796 | 17 | 896 |
"topicIds": [
"nearest-neighbour"
],
"lectureIds": [
"lecture-03",
"lecture-05"
],
"difficulty": "medium",
"cognitiveLevel": "conceptual",
"interactionType": "true_false",
"stem": "Increasing k in a k-NN classifier tends to reduce its ability to overfit ... | mlpc-exam | lib/data/question-bank.json | JSON | 62a83058de2079d03aa2872f907e97f739e2fc28e04c67b4de6b7178914294df | 18 | 896 |
,
"pages": [
6
],
"questionNumber": "23"
},
{
"sourceId": "lecture-07",
"pages": [
15
],
"note": "Automatically linked lecture slide by keyword overlap."
},
{
"sourceId": "lecture-07",... | mlpc-exam | lib/data/question-bank.json | JSON | 16874af70330199f40e762487f4c3e3a188090fcb4f3e32edac339ec1016508a | 19 | 896 |
architecture. gradient-descent neural-networks",
"groupId": "img-ann",
"choices": [
{
"id": "c1",
"text": "to find the optimal number of weights"
},
{
"id": "c2",
"text": "to find the smallest values for the weights that produce zero error"
... | mlpc-exam | lib/data/question-bank.json | JSON | c9d382f93ab8f86c0ab8d4e862925c079fbb36900f910a755d706f8565b878cd | 20 | 896 |
: "img-q026",
"origin": "real_closed_book_exam",
"sourceRefs": [
{
"sourceId": "img-closed-book-2026-p06",
"pages": [
6
],
"questionNumber": "26"
},
{
"sourceId": "lecture-07",
"pages": [
50
... | mlpc-exam | lib/data/question-bank.json | JSON | cb89fb7d42e40e483e94f7344db00d610b39b81aaf9c6143dbb4ea3b06a9c0f9 | 21 | 896 |
[
"learning-rate",
"gradient-descent"
],
"retrievalText": "The learning rate is used to ... The learning rate scales the gradient update step. learning-rate gradient-descent neural-networks",
"groupId": "img-ann",
"choices": [
{
"id": "c1",
"text": "co... | mlpc-exam | lib/data/question-bank.json | JSON | 95667d6360a835f160ef48f91108c0f212897017325a81a2310cf9c16eb451de | 22 | 896 |
true
},
{
"id": "img-q029",
"origin": "real_closed_book_exam",
"sourceRefs": [
{
"sourceId": "img-closed-book-2026-p07",
"pages": [
7
],
"questionNumber": "29"
},
{
"sourceId": "lecture-07",
"pages"... | mlpc-exam | lib/data/question-bank.json | JSON | 1278b1312226a0b4ef301426e501aec65e00d3dc13fb48e7731b7fad6412644a | 23 | 896 |
a global optimum, but it is not guaranteed in non-convex neural-network losses.",
"tags": [
"gradient-descent",
"optimization"
],
"retrievalText": "Parameter optimisation via iterative gradient descent ... Gradient descent may find a global optimum, but it is not guaranteed in non-conv... | mlpc-exam | lib/data/question-bank.json | JSON | eaac9841612a61462e54a3b08520625a4b91f384e357a742a044c70266a16bd0 | 24 | 896 |
: "Confusing forward-pass concepts with training, gradients, or parameter updates.",
"coreClosedBookLikely": true
},
{
"id": "img-q032",
"origin": "real_closed_book_exam",
"sourceRefs": [
{
"sourceId": "img-closed-book-2026-p07",
"pages": [
7
... | mlpc-exam | lib/data/question-bank.json | JSON | c41ec4572b4f99850908416143f1b56ca3ec3d843a55309627733fbd5cb7c70e | 25 | 896 |
neighbouring fully connected layers L1 and L2 have N and M units, respectively, the weight matrix for L1 to L2 including bias weights has ...",
"answerSource": "expert_verified",
"confidence": 0.96,
"explanation": "Each of M output units receives N incoming weights plus one bias weight.",
"tags"... | mlpc-exam | lib/data/question-bank.json | JSON | 70220cf25e36473401587ed4a2153a39861288e980204caf084e71739d749874 | 26 | 896 |
on the colour of x's k neighbours"
},
{
"id": "c5",
"text": "predicts the class of x based on the distance between x and its k-th neighbour"
}
],
"correctChoiceIds": [
"c1"
],
"correctAnswerText": "predicts the class of x based on a majority vo... | mlpc-exam | lib/data/question-bank.json | JSON | 31b933f21a10e68b2b75bc0f5414547a71e4f527d918ec7e5fc92067918baab9 | 27 | 896 |
{
"sourceId": "lecture-05",
"pages": [
42
],
"note": "Automatically linked lecture slide by keyword overlap."
},
{
"sourceId": "lecture-05",
"pages": [
43
],
"note": "Automatically linked lecture slid... | mlpc-exam | lib/data/question-bank.json | JSON | b676fdbda7a51a950a36c97f4702f220b3da8fd46b876d47ff52f5f524817d24 | 28 | 896 |
any feature test if their labels conflict. id3 consistency decision-trees",
"groupId": "img-learning-algorithms",
"choices": [
{
"id": "c1",
"text": "In an ID3 tree, nodes at the same level are labelled with the same feature"
},
{
"id": "c2",
"... | mlpc-exam | lib/data/question-bank.json | JSON | 6c849211763daee05ce3ee242a0fd5db72054b1b1656c3ee795a92c8752a2717 | 29 | 896 |
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