Tentative Class Schedule

All the lecture scribbles can be found here.

Lec Date Topic Mandatory Readings Scribbles Recordings Optional Readings
1 27 Aug 2025 Introduction, Prediction, Supervised Learning, Linear Model for Regression Logistics and Intro Slides, LN Chapter-01, Prince Chapter-01 Lecture-01 Scribble Lec0, Lec1A, Lec1B, Lec1C 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 LN Chapter-01, LN Chapter-02, Bishop section 1.1, 3.1, HTF section 2.3 Lecture-02 Scribble Lec2A, Lec2B, Lec2C The Saga of Highleyman’s Data by Moritz Hardt and Ben Recht.
3 10 Sep 2025 Geometry of Least Squares, Gradient Descent, Regularization, K-NN Regression LN Chapter-02, HTF section 3.4.1, 3.4.2 Lecture-03 Scribble Lec3A, Lec3B, Lec3C Implicit Gradient Regularization, Double Descent
4 17 Sep 2025 Decision Theory, Empirical Risk Minimization, Bias-variance Tradeoff, Classification LN Chapter-03, Chapter-04, HTF section 2.4, 2.5, Bishop section 3.2 Lecture-04 Scribble Lec4A, Lec4B, Lec4C  
5 24 Sep 2025 Probabilistic Generative Models, GDA, GLMs, Naive Bayes LN Chapter-03, Chapter-05, Chapter-06, Bishop section 4.2 Lecture-05 Scribble Lec5A, Lec5B, Lec5C  
  01 Oct 2025 For this week, lecture and lab slots are swapped!        
6 02 Oct 2025 [Lab Time] Naive Bayes, Logistic Regression, Newton-Raphson, Perceptron LN Chapter-06, Chapter-07, Chapter-08, Bishop section 4.2, 4.3, 4.1.7 Lecture-06 Scribble Lec6A, Lec6B, Lec6C Generative and discriminative classifiers by Tom Mitchell.
  08 Oct 2025 For this week, lecture and lab slots are swapped!        
7 09 Oct 2025 [Lab Time] Max-margin Classifiers, SVMs LN Chapter-08, Bishop section 7.1 Lecture-07 Scribble    
8 22 Oct 2025 Spectrum of classification algorithms, Evaluation Metrics, Decision Trees        
  23 Oct 2025 Mid-Term Exam [5 pm to 6:30 pm] The exam is based on the first seven lectures. Previous Exams      
9 29 Oct 2025 Ensembles: Bagging, Random Forests, Boosting, Stacking        
10 05 Nov 2025 Neural Nets, Backpropagation, Deep Neural Nets        
11 12 Nov 2025 Deep Neural Nets, Optimization        
12 19 Nov 2025 Bayesian Learning, MLE, MAP, Bayesian Linear Regression        
13 26 Nov 2025 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
3 11 Sep 2025 4:45 pm to 7:45 pm Linear Regression Recitation by Hadi Hojjati, followed by office hours for Assignment-1   Slides