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	01-17	CS 445 Introduction Introduction to ML	CH 1	PA0 Python Setup
	01-19	Trees and Data Decision Tree Activity (.pdf)	CH 2-2.1 CH 3-3.3 https://scipy-lectures.org/ (1.4.1-1.4.4) Fast Numpy Video		PA0 Part 1 Python Setup
2	01-24	Numpy Lab tree_warmup.ipynb (.html) decision_tree.py			PA0 A (Before class!)
	01-26	Bias/Variance Trade-off	App. C1-C1.1 Bias/Variance (1-2)	PA1	PA0 B (11:00PM)
3	01-31	Bias Variance Quiz (.pdf) Testing And Validation	CH 3.4-3.8
	02-02	Curse of Dimensionality	CH 2.2-2.4.5 CH 4.1, 4.3
4	02-07	ASSESSMENT DAY
	02-09	Class Imbalance KNN activity	CH 4.11
5	02-14	Probability Intro Probability Activity	CH 4.4-4.4.2	NB HW	PA1
	02-16	Engineering Candidate
6	02-21	Naive Bayes	CH 4.4.2	PA2
	02-23	Bagging boosting_exercises.pdf	CH 4.10-4.10.5		NB HW
7	02-28	EXAM 1
	03-02	Random Forest random_forest_quiz.pdf linear_algebra_exercises.pdf	CH 4.10.6 Linear Algebra Review (s) Section 1-3.7 (skip 3.6) Video (s) More Videos (s)
8	03-07	Gradient Descent Gradient Descent linear_regression.pdf linear_regression.py	deeplearningbook.org Section 4.3.0 Videos Calculus Refresher (S) 9-14
	03-09	Logistic Regression exercises	CH 4.6		PA2
9	03-14	SPRING BREAK
	03-16	SPRING BREAK
10	03-21	MLP pytorch_intro.ipynb	CH 4.7 PyTorch Tensors PyTorch Autograd	Poster Project
	03-23	PyTorch CIFAR 10 Lab	PyTorch Tutorial
11	03-28	Autodiff (.pdf) autodiff_exercise.pdf	Autodiff Video	PA3
	03-30	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		Poster Proposal
12	04-04	Convnets Convnets (.pdf) convnet_exercise.pdf	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)
	04-06	Word Embeddings RNNs rnn_exercises.ipynb	Word Embedding Tutorial "Unreasonable Effectiveness" deeplearningbook.org 10-10.2.0 (s)		Poster Bibliography
13	04-11	EXAM 2
	04-13	Transformers	LSTM Tutorial Attention Survey (Through Section 3)
14	04-18	SVM SVM Exercises (.pdf)	CH 4.9		PA3
	04-20	PCA pca.ipynb pca2.ipynb	Covariance Tutorial Video Appendix B.1.1
15	04-25	Nonlinear Dimensionality Reduction	PDSH: Manifold Learning
	04-27	Clustering Outlier Detection cluster_hw.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		Poster Submission
16	05-02	Poster Session
	05-04	Final Review
	05-09	Final Exam