Tentative schedule for CSCE 50603 -- 001 Machine Learning

Date	Topic	Lecture Slides	Additional Materials	Recomm. Reading
8/18	Introduction to machine learning	1-intro	01_intro.ipynb Anaconda.pdf data_code	Hastie Ch.1, Shwartz Ch.1 Zhang Ch.2
8/20
8/22	Software for machine learning 1
8/25	Preliminaries	2-preliminary	preliminary.ipynb
8/27	K nearest neighbors	3-knn	02_kNNs.ipynb	Hastie Ch.2.3.2, 2.3.3 Shwartz Ch.19.1
8/29	Linear regression and gradient descent	4-linear-regression	03_linear_regression.ipynb	Hastie Ch.3.1, 3.2, 3.4.1, 3.4.2 Shwartz Ch.9.2
9/1	Labor Day holiday
9/3	Linear regression and gradient descent
9/5	Bayes classifiers	5-bayes-classfier 5.5-measuring-errors	04_classification_metrics.ipynb	Hastie Ch.6.6.3 Shwartz Ch.24.2
9/8
9/10	Linear classifiers: perceptron, logistic regression, and SVM	6-linear-classifier 7-svm	05_logistic_regression.ipynb 06_SVM.ipynb	Hastie Ch.4.1, 4.4.1 Hastie Ch.12.1, 12.2
9/12
9/15
9/17
9/19	(Bonus) The kernel trick	8-kernel	07_SVM_RBF.ipynb	Hastie Ch.12.3.1
9/22	Decision tree	9-decision-tree	08_decision_tress.ipynb	Hastie Ch.9.2.1, 9.2.2, 9.2.3
9/24	Neural networks	10-nn	09_nn.ipynb	Hastie Ch.11.3, 11.4, 11.5
9/26
9/29
10/1
10/3	Half review
10/6	Mid-term exam
10/8	(Bonus) PAC learning			Hastie Ch.7.9 Murphy Ch.6.5.4
10/10	Software for machine learning 2
10/13	Fall Break
10/15	Hierarchical clustering			Hastie Ch.14.3.6, 14.3.12
10/17	K-means algorithm
10/20	EM algorithm			Hastie Ch.8.5
10/22	(Bonus) Fair machine learning
10/24
10/27	(Bonus) Intro to deep learning
10/29
10/31	(Bonus) Intro to reinforcement learning
11/3
11/5	(Bonus) Causal discovery and inference
11/7
11/10	Project presentations
11/12
11/14
11/17
11/19
11/21
11/24
11/26	Thanksgiving break
11/28
12/1	Final review
12/3	Q&A