Schedule

NoteCourse Materials
Lecture # Date Topic (Slides) Notebooks Reading
0 25 Jul Pre-requisite quiz released
1 6 Aug Introduction and Logistics Transcription and local LLMs, Segmentation and Localization, Prompt to Image
2 7 Aug Convention, Metrics, Classification, Regression Rule based vs ML, Confusion Matrix, Dummy baselines
3 8 Aug Decision Trees 1 Entropy, Decision Trees Real Output, DT Real Input Discrete Output
4 13 Aug Decision Trees 2 Decision Trees + Overfitting, Underfitting
5 14 Aug Bias-Variance Trade-off 1, Cross-Validation Tuning Hyperparameters, Bias-Variance Analysis, Bias-Variance Charts
6 20 Aug Ensemble Learning Boosting Explanation, Ensemble Feature Importance, Ensemble Representation
7 21 Aug Decision Tree Pruning, Bias-Variance Trade-off 2 Decision Tree Pruning Notebook
8 22 Aug Maths for ML, Contours & Gradients, Decision Tree Time Complexity Contour Plots,Meshgrid & Contours
9 27 Aug Linear Regression 1 Decision Tree Pruning Notebook
10 28 Aug Linear Regression 2 Geometric Linear Regression,Basis Expansion,Basis Expansion 2, Dummy Variables
11 29 Aug Gradient Descent Taylor’s series, Contours & Gradient Descent
12 3 Sep Movie recommendation & Matrix Factorization Movie Recommendation
13 10 Sep Ridge Regression Ridge Regression
14 11 Sep Lasso Regression Lasso Regression
15 12 Sep Constrained Optimization
16 17 Sep Logistic Regression 1 Why use logits, Logistic regression in Torch
17 8 Oct Logistic Regression 2 Why use logits, Logistic regression in Torch
18 9 Oct Tutorial on Convexity, Hessian, IRLS Notebook on 2nd Order Logistic Regression
19 10 Oct Precision-Recall Curves Notebook on PR Curves
20 15 Oct Multi-Layer Perceptrons Perceptron learning algorithm
21 16 Oct Automatic Differentiation Notebook on Autodiff
22 17 Oct Autograd from scratch
23 22 Oct Next Token Prediction Notebook, Streamlit App
24 23 Oct CNN - I Notebook 1, Notebook 2 on edge detection, Notebook 3 on LeNet