The schedule below represents my current best estimate concerning due dates (and everything else). I am providing this information to give you a general idea of the pace and timing of the class. This schedule will certainly change as the semester progresses.

Unless otherwise noted, all readings are from

• Introduction to Data Mining (Second Edition), Pang-Ning Tan et. al. Pearson, 2019

Readings followed by (S) are supplemental. You may find them helpful.

WEEK	DATE	TOPIC	READING/VIDEOS	OUT	IN
1	08-22	Introduction to ML	CH 1	Python Setup PA0
2	08-27	Decision Tree Activity (.pdf)	CH 2-2.1 CH 3-3.3		PA0 Part 1 Python Setup
	08-29	Numpy Lab tree_warmup.ipynb (.html) decision_tree.py	https://scipy-lectures.org/ (1.3.1-1.3.2) Fast Numpy Video		PA0 Part 2 (Before class!)
3	09-03	Expectation Bias/Variance Trade-off	App. C1-C1.1 Bias/Variance	PA1	PA0 Parts 3,4
	09-05	Bias Variance Quiz (.pdf) Testing And Validation	CH 3.4-3.8
4	09-10	Curse of Dimensionality	CH 2.2-2.4.5 CH 4.1, 4.3
	09-12	Class Imbalance KNN activity	CH 4.11
5	09-17	Probability Intro Probability Activity	CH 4.4-4.4.2	HW1	PA1
	09-19	Naive Bayes	CH 4.4.2	PA2
6	09-24	Bagging boosting_exercises.pdf	CH 4.10-4.10.5
	09-26	Random Forest random_forest_quiz.pdf	CH 4.10.6		HW1
7	10-01	EXAM 1
	10-03	linear_algebra_exercises.pdf Gradient Descent linear_regression.pdf linear_regression.py	Linear Algebra Review (S) Section 1-3.7 (skip 3.6) Video (S) More Videos (S) Calculus Refresher (S) 9-14 Neural Net Video 1/2 Neural Net Video 2/2 deeplearningbook.org Section 4.3.0
8	10-08	Logistic Regression	CH 4.6
	10-10	MLP	CH 4.7 PyTorch Tensors PyTorch Autograd		PA2
9	10-15	pytorch_intro.zip	PyTorch Tutorial
	10-17	FALL BREAK
10	10-22	CIFAR 10 Lab Remote Access Regularization etc.	CH 4.8 deeplearningbook.org 7 - 7.1.2, 7.4 7.12 (s) 8.2.2 - 8.2.4, 8.3.2	Poster Project
	10-24	Autodiff (.pdf) autodiff_exercise.pdf Autodiff	Autodiff Video	PA3
11	10-29	Convnets (.pdf) convnet_exercise.pdf	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)		Poster Proposal
	10-31	RNN rnn_exercises.ipynb MLP Review	Word Embedding Tutorial "Unreasonable Effectiveness" deeplearningbook.org 10-10.2.0 (s)
12	11-05	LSTM	LSTM Tutorial Transformer Tutorial
	11-07	EXAM 2
13	11-12	Transformers	ThreeBlueOneBrown 5 ThreeBlueOneBrown 6 ThreeBlueOneBrown 7		Poster Bibliography
	11-14	SVM	CH 4.9
14	11-19	PCA pca.ipynb pca2.ipynb	Covariance Tutorial Video Appendix B.1.1		PA3
	11-21	Nonlinear Dimensionality Reduction t-SNE Paper nonlinear_dr.zip	PDSH: Manifold Learning		Poster Draft
15	11-26	THANKSGIVING
	11-28	THANKSGIVING
16	12-03	Clustering	CH 7-7.2.2 CH 7.3-7.4 CH 7.5 (skim) CH 8.1 CH 9-9.3.1, 9.4 Skim 9.5-end
	12-05	Final Review