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 Lec7A, Lec7B, Lec7C  
8 22 Oct 2025 Spectrum of classification algorithms, Evaluation Metrics, Decision Trees LN Chapter-05, Chapter-06, Chapter-07, Chapter-09, Bishop section 4.1.1, 4.1.2, TSKK section 3.3, Mitchell Chapter 03 Lecture-08 Scribble Lec8A, Lec8B, Lec8C A Few Useful Things to Know about Machine Learning by Pedro Domingos
  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 LN Chapter-10 ,Bishop Chapter 14, Stacking paper Lecture-09 Scribble Lec9A, Lec9B, Lec 9C  
  05 Nov 2025 Class Cancelled        
10 12 Nov 2025 Neural Nets, Backpropagation, Deep Neural Nets LN Chapter-11, Rojas Chapter 7 Lecture-10 Scribble Lec10A, Lec10B, Lec10C  
11 19 Nov 2025 Deep Neural Nets, Optimization, Dimensionality Reduction LN Chapter-11, LN Chapter-12, LN Chapter-14 Lecture-11 Scribble Lec11A, Lec11B, Lec11C  
12 26 Nov 2025 Bayesian Learning, MLE, MAP, Bayesian Linear Regression LN Chapter-15, Bishop Chapter 3.3, Parameter Estimation Lecture-12 Scribble Lec12A, Lec12B, Lec12C  
13 27 Nov 2025 [Lab Time] Frontiers in ML, Online Learning, Continual Learning   Lecture-13 Scribble    
  12 Dec 2025 Final Exam [9:30 am to 12:00 pm] The exam is based on all 13 lectures. Previous Exams      

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