Schedule
Tentative Class Schedule
All the lecture scribbles can be found here.
| Lec | Date | Topic | Mandatory Readings | Optional Readings |
|---|---|---|---|---|
| 1 | 28 Aug 2024 | 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 | 04 Sep 2024 | 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 | |
| 3 | 11 Sep 2024 | Geometry of Least Squares, Gradient Descent, Regularization, K-NN Regression | LN Chapter-02, HTF section 3.4.1, 3.4.2 | Implicit Gradient Regularization |
| 4 | 18 Sep 2024 | Decision Theory, Empirical Risk Minimization, Bias-variance Tradeoff, Classification | LN Chapter-03, Chapter-04, HTF section 2.4, 2.5, Bishop section 3.2 | |
| 5 | 25 Sep 2024 | Probabilistic Generative Models, GDA, GLMs, Naive Bayes | LN Chapter-03, Chapter-05, Chapter-06, Bishop section 4.2 | |
| 6 | 02 Oct 2024 | Naive Bayes, Logistic Regression, Newton-Raphson, Perceptron | LN Chapter-06, Chapter-07, Chapter-08, Bishop section 4.2, 4.3, 4.1.7 | Generative and discriminative classifiers by Tom Mitchell. |
| 7 | 09 Oct 2024 | Perceptron, Max-margin Classifiers, SVMs | LN Chapter-08, Bishop section 7.1 | The Saga of Highleyman’s Data by Moritz Hardt and Ben Recht. |
| 8 | 23 Oct 2024 | 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 | A Few Useful Things to Know about Machine Learning by Pedro Domingos |
| 24 Oct 2024 | Mid-Term Exam [5 pm to 6:30 pm] | Last year Mid-term Questions | ||
| 9 | 30 Oct 2024 | Ensembles: Bagging, Random Forests, Boosting, Stacking | LN Chapter-10 ,Bishop Chapter 14, Stacking paper | |
| 10 | 06 Nov 2024 | Neural Nets, Backpropagation, Deep Neural Nets | LN Chapter-11, Rojas Chapter 7 | |
| 11 | 13 Nov 2024 | Deep Neural Nets, Optimization | LN Chapter-11, LN Chapter-12 | |
| 12 | 20 Nov 2024 | Bayesian Learning, MLE, MAP, Bayesian Linear Regression | LN Chapter-15, Bishop Chapter 3.3, Parameter Estimation | |
| 13 | 27 Nov 2024 | Tips and Tricks in ML, Frontiers in ML, What Next? | ||
| 12 Dec 2024 | Final Exam [9:30 am to 12:00 pm] | Last year final exam | Final exam is based on all 13 weeks of lectures. |
Tutorials
| Lec | Date | Time | Topic | Lecture Videos | Lecture Materials |
|---|---|---|---|---|---|
| 1 | 29 Aug 2024 | No lab. Use the lab time for reading. | |||
| 2 | 05 Sep 2024 | 4:45 pm - 7:45 pm | Online Office Hours by Hadi Hojjati (link in Piazza) | ||
| 3 | 12 Sep 2024 | 4:45 pm - 7:45 pm | Online Office Hours by Hadi Hojjati (link in Piazza) | ||
| 4 | 19 Sep 2024 | No lab. Use the lab time for doing the assignment. | |||
| 24 Sep 2024 | 4:45 pm - 6:45 pm | Online Office Hours by Hadi Hojjati (link in Piazza) | |||
| 5 | 26 Sep 2024 | 4:45 pm - 6:45 pm | Online Office Hours by Hadi Hojjati (link in Piazza) | ||
| 6 | 03 Oct 2024 | 4:45 pm - 7:45 pm | In person Office Hours by Megh Thakkar | ||
| 7 | 10 Oct 2024 | 4:45 pm - 6:15 pm | Scikit-Learn Tutorial by Maryam Hashemzadeh (in person) | Recording | Notebook |
| 10 Oct 2024 | 6:15 pm - 7:45 pm | Online Office Hours by Megh Thakkar (link in Piazza) | |||
| 16 Oct 2024 | 11 am - 12 pm | Online Office Hours by Sarath Chandar | |||
| 16 Oct 2024 | 5:45 pm - 7:45 pm | In person Office Hours by Megh Thakkar | |||
| 21 Oct 2024 | 5:00 pm - 7:00 pm | Online Office Hours by Megh Thakkar (link in Piazza) | |||
| 8 | 24 Oct 2024 | 5 pm to 6:30 pm | Mid-term Exam | ||
| 9 | 31 Oct 2024 | ||||
| 10 | 07 Nov 2024 | ||||
| 11 | 14 Nov 2024 | ||||
| 12 | 21 Nov 2024 | ||||
| 13 | 29 Nov 2024 |