Schedule

Summary
Lecture # Date Topic (Slides) Notebooks Reading
0 25 Jul Pre-requisite quiz released
1 2 Aug Introduction and Logistics Transcription and local LLMs, Segmentation and Localization, Prompt to Image
2 6 Aug Convention, Metrics, Classification, Regression Rule based vs ML, Confusion Matrix, Dummy baselines
3 7 Aug Decision Trees 1 Entropy, Decision Trees Real Output, DT Real Input Discrete Output
4 9 Aug Decision Trees 2, Bias and Variance Bias variance, Decision Trees + Overfitting, Underfitting
5 13 Aug Tutorial on Numpy, Pandas, Classes, Cross-validation Numpy, Pandas, Entropy, Classes and Trees, Cross-validation and tuning hyperparameters
6 16 Aug Lecture on Random Forests Notebook on Random Forests feature importance from scratch
7 20 Aug Tutorial on RF feature importance, meshgrid, contour plots Notebook on Random Forests feature importance from scratch, Notebook on meshgrid
8 21 Aug Linear Regression
9 23 Aug Linear Regression: Basis Functions Notebook
10 27 Aug Linear Regression: Geometric Interpretation, Multi-colinearity, Dummy Variables Notebook, Notebook on multi-colinearlity
11 29 Aug Gradient Descent Notebook on Taylor’s series, Notebook on Contour plots and gradient descent
12 30 Aug
13 3 Sep Quiz
14 5 Sep Quiz discussion
15 6 Sep Gradient Descent Notebook on Taylor’s series, Notebook on Contour plots and gradient descent
16 10 Sep Movie recommendation, Matrix Factorisation Notebook on Movie Recommendation
17 12 Sep Regularised Linear Regression: Ridge Regression, LASSO Regression
18 13 Sep Logistic Regression Notebook on why use logits, Notebook on Logistic regression in Torch
19 17 Sep Logistic Regression Notebook on why use logits, Notebook on Logistic regression in Torch
20 19 Sep Tutorial on convexity
21 20 Sep Weighted Linear regression, Convexity of Cross-entropy loss function for logistic regression and IRLS
22 24 Sep Precision recall curve, MLP-1 Notebook on precision recall curves
23 26 Sep MLP I
24 15 Oct MLP II
25 17 Oct Autograd Notebook
26 18 Oct Next word prediction [Slides], Notebook
28 22 Oct CNN - I, 1D CNN Notebook 1, Notebook 2 on edge detection, Notebook 3 on LeNet
29 24 Oct CNN - II
30 25 Oct Object detection tutorial + How to setup Kaggle
31 29 Oct Unsupervised learning
32 1 Nov KNN, Approximate KNN Notebook (Parametric v/s Non-Parametric), Notebook (Curse of dimensionality)
33 5 Nov Reinforcement learning Notebook 1, Notebook 2
34 7 Nov SVM - I Notebook 1
35 12 Nov [SVM-2 Kernels (slides from previous lecture)] Notebook, Notebook 2
36 14 Nov Constrained Optimization 1, Constrained Optimization 2 (self study) Part 2 recording from older run
37 19 Nov Support Vector Machines (Soft Margin)