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	01-16	Introduction to ML	CH 1	PA0 Python Setup
	01-18	Trees and Data Decision Tree Activity (.pdf)	CH 2-2.1 CH 3-3.3		PA0 Part 1 Python Setup
2	01-23	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!)
	01-25	Expectation Bias/Variance Trade-off	App. C1-C1.1 Bias/Variance	PA1	PA0 Part 3
3	01-30	Bias Variance Quiz (.pdf) Testing And Validation	CH 3.4-3.8
	02-01	Curse of Dimensionality	CH 2.2-2.4.5 CH 4.1, 4.3
4	02-06	ASSESSMENT DAY
	02-08	Class Imbalance KNN activity	CH 4.11
5	02-13	Probability Intro Probability Activity	CH 4.4-4.4.2	HW1	PA1
	02-15	Naive Bayes	CH 4.4.2
6	02-20	Bagging boosting_exercises.pdf	CH 4.10-4.10.5	PA2
	02-22	Random Forest random_forest_quiz.pdf	CH 4.10.6		HW1
7	02-27	EXAM 1
	02-29	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	03-05	Logistic Regression	CH 4.6
	03-07	MLP pytorch_intro.ipynb	CH 4.7 PyTorch Tensors PyTorch Autograd		PA2
9	03-12	SPRING BREAK
	03-14	SPRING BREAK
10	03-19	PyTorch CIFAR 10 Lab	PyTorch Tutorial	Poster Project
	03-21	Regularization etc. Neural Net Questions (.pdf)	CH 4.8 deeplearningbook.org 7 - 7.1.2, 7.4 7.12 (s) 8.2.2 - 8.2.4, 8.3.2
11	03-26	Autodiff (.pdf) autodiff_exercise.pdf	Autodiff Video	PA3
	03-28	Convnets (.pdf) convnet_exercise.pdf Remote Access	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)		Poster Proposal
12	04-02	RNN rnn_exercises.ipynb	Word Embedding Tutorial "Unreasonable Effectiveness" deeplearningbook.org 10-10.2.0 (s)
	04-04	LSTM	LSTM Tutorial Transformer Tutorial		Poster Bibliography
13	04-09	EXAM 2
	04-11	Transformers
14	04-16	SVM SVM Exercises (.pdf)	CH 4.9		PA3
	04-18	PCA pca.ipynb pca2.ipynb	Covariance Tutorial Video Appendix B.1.1
15	04-23	Nonlinear Dimensionality Reduction nonlinear_dr.zip	PDSH: Manifold Learning		Poster Draft
	04-25	clustering_activity.pdf	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
16	04-30	Final Review
	05-02	Exam