ML2024 | Schedule

Date	Lecture	Readings	Logistics
Tues, 2024/02/20	Lecture #1 : Introduction to Machine Learning [ slides ]	Rademacher Complexity and VC-Dimension
Thurs, 2024/02/22	Lecture #2 : Introduction to Machine Learning (continued), Linear Regression [ slides ]
Tues, 2024/02/27	Lecture #3 : Linear Regression (continued) [ slides ]	Newton's Method & Gradient Descent Maximum Liklihood Estimation
Thurs, 2024/02/29	Lecture #4 : Linear Regression (continued), Logistic Regression [ slides ]
Tues, 2024/03/05	Lecture #5 : Logistic Regression (continued) [ slides ]	Convexity	HW1 Out
Thurs, 2024/03/07	Lecture #6 : Logistic Regression (continued) [ slides ]	Taylor series
Tues, 2024/03/12	Lecture #7 : Model Selection & Evaluation [ slides ]	Multi-class and Multi-labels
Thurs, 2024/03/14	Lecture #8 : Model Selection & Evaluation (continued) [ slides ]	The Relationship Between Precision-Recall and ROC Curves	Lab1 Out
Tues, 2024/03/19	Lecture #9 : Linear Discriminant Analysis [ slides ]	QR algorithm Gram–Schmidt process
Thurs, 2024/03/21	Lecture #10 : Linear Discriminant Analysis [ slides ]	Covariance matrix Mahalanobis distance
Tues, 2024/03/26	Lecture #11 : Support Vector Machine (Formulation) [ slides ]		HW1 Due
Thurs, 2024/03/28	Lecture #12 : Support Vector Machine (Optimization) [ slides ]	Primal and dual problems Duality and SVM	Lab2 Out
Tues, 2024/04/02	Lecture #13 : Support Vector Machine (Soft margin) [ slides ]	Duality Theory	Lab1 Due; HW2 Out
Thurs, 2024/04/04	No class（清明节放假)
Tues, 2024/04/09	Lecture #14 : Support Vector Machine (Kernel) [ slides ]	The Representer Theorem
Thurs, 2024/04/11	Lecture #15 : Neural Networks: Introduction [ slides ]
Tues, 2024/04/16	Lecture #16 : Neural Networks: Introduction (continued) [ slides ]		Lab2 Due
Thurs, 2024/04/18	Lecture #17 : Backpropagation [ slides ]
Tues, 2024/04/23	Lecture #18 : Backpropagation [ slides ]		HW2 Due
Thurs, 2024/04/25	Lecture #19 : Convolutional Neural Network [ slides ]		Lab3 Out
Sun, 2024/04/28	Lecture #20 : Recurrent Neural Network [ slides ]
Tues, 2024/04/30	Lecture #21 : Recurrent Neural Network [ slides ]		HW3 Out
Tues, 2024/05/07	Lecture #22 : Decision Tree [ slides ]	Information, Entropy, and Coding
Thurs, 2024/05/09	Lecture #23 : Decision Tree (continued) [ slides ]
Tues, 2024/05/14	Lecture #24 : Bayesian Classifier [ slides ]		Lab3 Due
Thurs, 2024/05/16	Lecture #25 : Bayesian Classifier (continued) [ slides ]	Naive Bayes, Text Classification Naive Bayes (lecture@Cornell)
Tues, 2024/05/21	Lecture #26 : Bayesian Classifier (continued) [ slides ]	The Expectation Maximization Algorithm	HW3 Due; HW4 Out
Thurs, 2024/05/23	Lecture #27 : Ensemble [ slides ]	AdaBoost Bagging, AdaBoost	Lab4 Out
Tues, 2024/05/28	Lecture #28 : Ensemble [ slides ]	Boosting (lecture@MIT)
Thurs, 2024/05/30	Lecture #29 : Clustering [ slides ]
Tues, 2024/06/04	Lecture #30 : Clustering [ slides ]
Thurs, 2024/06/06	Lecture #31 : Review, Q&A [ slides ]