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
	08-26	Introduction to ML decision_tree_activity.pdf	CH 1	PA0 Python Setup
2	08-31	Types of Data Decision Tree Activity (.pdf) Numpy Lab	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
	09-02	decision_tree.py tree_warmup.ipynb (.html)			PA0 A (Before class!)
3	09-07	Bias/Variance Trade-off	App. C1-C1.1 Bias/Variance (1-2)	PA1	PA0 B (11:00PM)
	09-09	Bias Variance Quiz (.pdf) Testing And Validation	CH 3.4-3.8
4	09-14	Curse of Dimensionality KNN	CH 2.2-2.4.5 CH 4.1, 4.3
	09-16	Class Imbalance	CH 4.11
5	09-21	Probability Review Probability Intro	CH 4.4-4.4.2	NB HW
	09-23	Naive Bayes Exam Review	CH 4.4.2		PA1
6	09-28	EXAM 1
	09-30	Bagging boosting_exercises.pdf	CH 4.10-4.10.5	PA2	NB HW
7	10-05	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)
	10-07	Gradient Descent linear_regression.pdf linear_regression.py	deeplearningbook.org Section 4.3.0 Videos Calculus Refresher (S) 9-14
8	10-12	Logistic Regression	CH 4.6
	10-14	MLP Tensorflow Lab (.ipynb)	CH 4.7
9	10-19	Keras CIFAR 10 Lab	Keras Intro Tutorial Keras Validation Keras Save/Load		PA2
	10-21	FALL BREAK
10	10-26	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 Project
	10-28	Autodiff (.pdf) autodiff_exercise.pdf	Autodiff Video	PA3
11	11-02	Convnets (.pdf) convnet_exercise.pdf	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)
	11-04	EXAM 2			Poster Proposal
12	11-09	Word Embeddings RNNs rnn_exercises.ipynb (.html)	Word Embedding Tutorial "Unreasonable Effectiveness" Tensorflow implementation (s) deeplearningbook.org 10-10.2.0 (s)
	11-11	Transformers	LSTM Tutorial Attention Survey (Through Section 3)		Poster Bibliography
13	11-16	SVM SVM Exercises (.pdf)	CH 4.9
	11-18	PCA pca.ipynb pca2.ipynb	Covariance Tutorial Video Appendix B.1.1		PA3
14	11-23	THANKSGIVING
	11-25	THANKSGIVING
15	11-30	Nonlinear Dimensionality Reduction nonlinear.pdf	PDSH: Manifold Learning
	12-02	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
16	12-07	Final Review			Poster Submission
	12-09	Final Exam
	12-14	Poster Session 10:30-12:30