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	01-19	Introduction to ML Intro Decision Trees	CH 1	PA0 Python Setup
	01-21	Types of Data Decision Tree Activity (.pdf)	CH 2-2.1 CH 3-3.3
2	01-26	Numpy Lab	https://scipy-lectures.org/ (1.4.1-1.4.4) Fast Numpy Video		PA0 A (Before class!)
	01-28	decision_tree.py tree_warmup.ipynb
3	02-02	Bias/Variance Trade-off	App. C1-C1.1 Bias/Variance (1-2)	PA1	PA0 B (11:00PM)
	02-04	Testing And Validation	CH 3.4-3.8
4	02-09	ASSESSMENT DAY
	02-11	Curse of Dimensionality KNN	CH 2.2-2.4.5 CH 4.1, 4.3
5	02-16	Class Imbalance + ROC Curves	CH 4.4-4.4.1 CH 4.11
	02-18	EXAM 1 Review
6	02-23	Probability Review Naive Bayes	CH 4.4.2	NB HW	PA1
	02-25	Bagging boosting_exercises.pdf	CH 4.10-4.10.5	PA2	NB HW
7	03-02	Random Forest Boosting random_forest_quiz.pdf	CH 4.10.6
	03-04	Linear Algebra Review (.pdf)	Linear Algebra Review Section 1-3.7 (skip 3.6) Video More Videos (S)
8	03-09	Gradient Descent	deeplearningbook.org Section 4.3.0 Videos Calculus Refresher (S) 9-14	Poster Project
	03-11	Logistic Regression exercises	CH 4.6
9	03-16	MLP Tensorflow Lab (.ipynb)	CH 4.7		PA2
	03-18	Keras CIFAR 10 Lab	Keras Intro Tutorial Keras Validation Keras Save/Load
10	03-23	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
	03-25	Autodiff (.pdf)	Autodiff Video
11	03-30	Convnets (.pdf) convnet_exercise.pdf	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)	PA3
	04-01	Word Embeddings Start RNN rnn_exercises.ipynb	Word Embedding Tutorial "Unreasonable Effectiveness" Tensorflow implementation (s) deeplearningbook.org 10-10.2.0 (s)
12	04-06	Transformers	LSTM Tutorial Attention Survey (Through Section 3)		Poster Bibliography
	04-08	BREAK DAY
13	04-13	SVM	CH 4.9		PA3
	04-15	PCA Lab pca.ipynb pca2.ipynb	Covariance Tutorial Video Appendix B.1.1
14	04-20	Nonlinear Dimensionality Reduction nonlinear.pdf	PDSH: Manifold Learning
	04-22	Clustering cluster_hw.pdf	CH 7-7.2.2 CH 7.3-7.4 CH 7.5 (skim) CH 8.1
15	04-27	Outlier Detection mystery.npy	CH 9-9.3.1, 9.4 Skim 9.5-end
	04-29	Final Review			Poster Submission
16	05-04	Final Exam 10:30-12:30AM