Tentative Class Schedule

All lecture scribbles are available here.

Lecture Notes for the course are available here. Lecture notes chapters will be referred to in the references as LN Chapter-01, LN Chapter-02, etc.

Lec Date Topic Reference Readings
1 30 Aug 2023 Introduction to ML, Linear Regression Slides, LN Chapter-01, Murphy Chapter 01 Computing Machinery and Intelligence by Alan Turing,
Probability Review,
Linear Algebra Review
2 06 Sep 2023 Overfitting, ML Pipeline, Cross-validation, Linear Models with Non-linear Basis Functions, Geometry of Least Squares LN Chapter-02, Bishop section 1.1, 3.1, HTF section 2.3 Linear Algebra review (7.1) in this pdf.
3 13 Sep 2023 Gradient Descent, Regularization, K-NN Regression LN Chapter-03, HTF section 3.4.1, 3.4.2 Implicit Gradient Regularization(optional), Double Descent(optional)
4 20 Sep 2023 Decision Theory, Empirical Risk Minimization, Bias-variance Tradeoff, Classification LN Chapter-03, Chapter-04, HTF section 2.4, 2.5, Bishop section 3.2 Linear Algebra review (7.1) in this pdf. Lagrange Multiplier (Appendix E in Bishop)
5 27 Sep 2023 Probabilistic Generative Models, GDA, GLMs, Naive Bayes LN Chapter-03, Chapter-05, Chapter-06, Bishop section 4.2 Generative and discriminative classifiers by Tom Mitchell.
6 04 Oct 2023 Naive Bayes, Logistic Regression, Newton-Raphson, Perceptron LN Chapter-06, Chapter-07, Bishop section 4.2, 4.3, 4.1.7 The Saga of Highleyman’s Data by Moritz Hardt and Ben Recht.
7 18 Oct 2023 Max-margin Classifiers, SVMs LN Chapter-08, Bishop section 7.1 A Few Useful Things to Know about Machine Learning by Pedro Domingos
  19 Oct 2023 Mid-Term Exam (5 pm to 6:30 pm) Sample Questions, Self-Assessment Questions  
8 25 Oct 2023 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  
9 01 Nov 2023 Ensembles: Bagging, Random Forests, Boosting, Stacking LN Chapter-10 ,Bishop Chapter 14, Stacking paper  
10 08 Nov 2023 Neural Nets, Backpropagation, Deep Neural Nets LN Chapter-11, Rojas Chapter 7.  
11 15 Nov 2023 Deep Neural Nets, ConvNets, Optimization LN Chapter-11, LN Chapter-13, LN Chapter-12, ConvNets Memory-augmented Optimizers
12 22 Nov 2023 Bayesian Learning, MLE, MAP, Bayesian Linear Regression LN Chapter-15, Bishop Chapter 3.3, Parameter Estimation  
13 29 Nov 2023 Dimensionality Reduction, PCA, LDA, Tips and Tricks in ML, Frontiers in ML, What Next? LN Chapter-14, Chapter-17, Bishop section 4.1.4, PCA  
  22 Dec 2023 Final Exam (9:30 am to 12 pm) Mid-Term Paper, Self-Assessment Questions  

Tutorials

Lec Date Time Topic Lecture Videos Lecture Materials
0     Pre-req: Probability Video Slides
0     Pre-req: Linear Algebra Video Notebook
0     Pre-req: Python, Numpy, Plotting Video Notebook
1 28 Sep 2023 5 pm to 6 pm Scikit-Learn Video Notebook
2 07 Nov 2023 5 pm to 6 pm PyTorch Video Notebook

Reference Materials

The course is based on the following references.

  1. [LN] INF8245E Course Lecture Notes.
  2. [HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Available free online.
  3. [Bishop] Christopher Bishop. Pattern Recognition and Machine Learning.
  4. [Mitchell] Tom Mitchell. Machine Learning.
  5. [TSKK] Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. Introduction to Data Mining.
  6. [Rojas] Raul Rojas. Neural Networks.
  7. [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. Available free online
  8. [Murphy] Kevin P. Murphy. Probabilistic Machine Learning: An Introduction. free online