Schedule

Summary
Lecture # Date Topic (Slides) Notebooks YouTube Recording Reading
0 30 Dec 2023 Pre-requisite quiz released
1 3 Jan Introduction and Logistics Transcription and local LLMs, Segmentation and Localization, Prompt to Image Recording
2 5 Jan Convention, Metrics, Classification, Regression Rule based vs ML, Confusion Matrix, Dummy baselines Recording
3 8 Jan Decision Trees 1 Entropy, Decision Trees Real Output, DT Real Input Discrete Output Recording
4 10 Jan Decision Trees 2, Bias and Variance Bias variance, Decision Trees + Overfitting, Underfitting Recording
5 12 Jan Tutorial on Numpy, Pandas, Classes, Cross-validation Numpy, Pandas, Entropy, Classes and Trees, Cross-validation and tuning hyperparameters Tutorial recording, Lecture on Cross-validation
6 15 Jan Cross-validation, Ensemble Methods Cross-validation and tuning hyperparameters Recording
7 17 Jan Lecture on Random Forests , Tutorial on Random Forests + Meshgrid Video Notebook on Random Forests feature importance from scratch, Notebook on meshgrid
8 19 Jan Linear Regression Recording
9 24 Jan Linear Regression: Basis Functions Recording Notebook
10 25 Jan Linear Regression: Geometric Interpretation Recording Notebook
10 27 Jan Multi-colinearity, Dummy Variables Recording Notebook
11 29 Jan Gradient Descent Recording Notebook on Taylor’s series, Notebook on Contour plots and gradient descent
12 31 Jan Tutorial on GCloud + Forecasting + Polynomial Regression with Basis functions Notebook on Polynomial Regression, Notebook on Autoregressive Model Article on Polynomial Regression
13 2 Feb Quiz 1
14 5 Feb Gradient Descent Recording Notebook on Gradient Descent
15 7 Feb Movie recommendation, Matrix Factorisation Recording Notebook on Movie Recommendation
16 9 Feb Regularised Linear Regression: Ridge Regression, LASSO Regression Recording
17 12 Feb Coordinate Descent + Time Complexity + ML Maths + Convexity Not Recorded Notebook on Sklearn with GPU
18 14 Feb Mock Quiz
19 16 Feb Guest Lecture by Arjun Bhagoji, Adversarial Examples for ML Notebook   
20 26 Feb Mid sem exam discussion
21 28 Feb Logistic Regression Recording Notebook on why use logits, Notebook on Logistic regression in Torch
22 1 March Logistic Regression Recording Notebook
23 5 March MLP I Recording
24 6 March MLP II Recording Notebook
25 7 March Next token prediction Recording Notebook
26 11 March Mock Quiz Solution
27 13 March Autograd Recording Notebook
28 18 March CNN - I, 1D CNN Recording Notebook 1, Notebook 2 on edge detection, Notebook 3 on LeNet
29 20 March CNN - II Recording Notebook
30 22 March KNN Recording Notebook (Parametric v/s Non-Parametric), Notebook (Curse of dimensionality) Reference on Parameteric v/s Non-Parametric from MLSS, ML Mastery, StackExchange, Sebastian Raschka’s blog
31 1 April Reinforcement learning Recording Notebook 1, Notebook 2
32 3 April Unsupervised learning Recording Notebook
33 5 April Approximate KNN Recording Notebook
34 8 April Constrained Optimization 1, Constrained Optimization 2 (self study) Part 2 recording from older run Notebook
35 10 April SVM - I Recording Notebook 1
36 12 April SVM - II (Kernel) Recording Notebook, Notebook 2
37 15 April SVM - III (QP, Kernels as similarity) Recording Notebook on QP, Notebook on kernels-1
38 17 April SVM - IV (Kernel understanding, Soft Margin) Recording Notebook on kernel understanding, Notebook on soft margin
39 19 April Overall revision