Tentative Class Schedule

All the lecture scribbles can be found here.

Lec Date Topic Mandatory Readings Optional Readings
1 27 Aug 2025 Introduction, Prediction, Supervised Learning, Linear Model for Regression Logistics and Intro Slides, LN Chapter-01, Prince Chapter-01 Computing Machinery and Intelligence by Alan Turing, Probability Review, Linear Algebra Review
2 03 Sep 2025 Linear Regression, Overfitting, ML Pipeline, Cross-validation, Linear Models with Non-linear Basis Functions, Geometry of Least Squares LN Chapter-01, LN Chapter-02, Bishop section 1.1, 3.1, HTF section 2.3 The Saga of Highleyman’s Data by Moritz Hardt and Ben Recht.
3 10 Sep 2025 Gradient Descent, Regularization, K-NN Regression LN Chapter-02, HTF section 3.4.1, 3.4.2  
4 17 Sep 2025 Decision Theory, Empirical Risk Minimization, Bias-variance Tradeoff, Classification    
5 24 Sep 2025 Probabilistic Generative Models, GDA, GLMs, Naive Bayes    
  01 Oct 2024 For this week, lecture and lab slots are swapped!    
6 02 Oct 2024 [Lab Time] Logistic Regression, Newton-Raphson, Perceptron    
7 08 Oct 2024 Max-margin Classifiers, SVMs    
8 22 Oct 2024 Spectrum of classification algorithms, Evaluation Metrics, Decision Trees    
  23 Oct 2024 Mid-Term Exam [5 pm to 6:30 pm]    
9 29 Oct 2024 Ensembles: Bagging, Random Forests, Boosting, Stacking    
10 05 Nov 2024 Neural Nets, Backpropagation, Deep Neural Nets    
11 12 Nov 2024 Deep Neural Nets, Optimization    
12 19 Nov 2024 Bayesian Learning, MLE, MAP, Bayesian Linear Regression    
13 26 Nov 2024 Frontiers in ML, Online Learning, Continual Learning    
    Final Exam   Final exam is based on all 13 weeks of lectures and labs.

Tutorials

Lec Date Time Topic Lecture Videos Lecture Materials
1 28 Aug 2025 4:45 pm to 7:45 pm Review of Probability and Linear Algebra by Hadi Hojjati   Probability, Linear Algebra
2 04 Sep 2025 4:45 pm to 6:30 pm Introduction to Pandas by David Heurtel-Depeiges   Notebook