Tentative Class Schedule

Self-assessment questions for each lecture are available here.

Scribbles for each lecture are available here.

Lec Date Topic Lecture Notes Reference Readings
1 31 Aug 2022 Intro to ML, Linear Regression Chapter-1 Bishop section 1.1 ,
HTF section 2.3		 Computing Machinery and Intelligence by Alan Turing,
Probability Review,
Linear Algebra Review
2 07 Sep 2022 Overfitting, ML Pipeline, Classification, k-NN, More on Regression, Gradient Descent, Regularization Chapter-1
Chapter-2		 Bishop section 1.1, 3.1, 3.1.4,
HTF section 2.3, 2.4, 2.5 ,3.4.1, 3.4.2		  
3 14 Sep 2022 Gradient Descent, Regularization, Decision theory, Empirical Risk Minimization, Bias-Variance Tradeoff Chapter-2, Chapter-3,
Chapter-4		 Bishop section 3.1, 3.1.4
HTF section 3.4.1, 3.4.2, 2.4, 2.5		 Linear Algebra review (7.1) in this pdf. Lagrange Multiplier (Appendix E in Bishop)
4 21 Sep 2022 Linear Classification, GLM, GDA Chapter-4
Chapter-5
Chapter-6		 Bishop: Chapter 3 section 3.2
HTF: Chapter 2:section 2.9 and Chapter 10		 Intro to convex optimization (page 91 to 102 in this book)
5 28 Sep 2022 GDA, Naive Bayes, Logistic Regression, Evaluation Metrics Chapter-6
Chapter-7		 Bishop: Chapter 5;section 1 and section 2 Generative and discriminative classifiers by Tom Mitchell.
6 05 Oct 2022 Evaluation Metrics, Newton-Raphson method, Perceptron, Max-margin Classifiers, SVMs Chapter-7
Chapter-8		    
7 19 Oct 2022 SVMs, Decision Trees Chapter-8, Chapter-09   The Saga of Highleyman’s Data by Moritz Hardt and Ben Recht.
8 26 Oct 2022 Decision Trees, Ensembles: Bagging, Random Forests, Stacking Chapter-10    
9 02 Nov 2022 Neural Nets, Backpropagation, Deep Neural Networks Chapter-11   A Few Useful Things to Know about Machine Learning by Pedro Domingos
10 09 Nov 2022 Optimization, Convolutional Networks Chapter-12
Chapter-13		    
11 16 Nov 2022 Dimensionality Reduction, PCA, LDA, Bayesian learning, MLE, MAP Chapter-14
Chapter-15		    
12 23 Nov 2022 Bayesian Linear Regression, Kernel Methods, Gaussian Process Chapter-15
Chapter-16		    
13 30 Nov 2022 Tips and Tricks, Frontiers in ML, What Next?      

Tutorials

Lec Date Time Topic Lecture Videos Lecture Materials
1 02 Sep 2022 9 am to 10:30 am Probability Video Slides
2 06 Sep 2022 9 am to 10:30 am Python, Numpy, Plotting Video Notebook
3 09 Sep 2022 9 am to 10:30 am Linear Algebra Video Notebook
4 28 Oct 2022 9 am to 10:30 am Scikit-learn Video Notebook
5 11 Nov 2022 9 am to 10:30 am PyTorch   Notebook