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	01-13	Intro: What is ML?	CH 1	PA0 Python Config
	01-15	Bias/Variance Lab	App. C1-C1.1 Bias/Variance Decomposition (1-2)		Python Config
	01-17	Bias/Variance Quiz Dimensionality Lab	CH 2-2.3.3
2	01-20	MLK Day
	01-22	Numpy Lab	https://scipy-lectures.org/ (1.3.1-1.3.4) Fast Numpy Video
	01-24	Classification & Decision Trees	CH 3-3.3
3	01-27	Decision Trees tree_warmup.py		PA1	PA0
	01-29	Testing and Validation Lab	CH 3.4-3.8
	01-31	Bagging	CH 4.10-4.10.5
4	02-03	Random Forests Ensemble Activity	CH 4.10.6	Poster Project
	02-05	K-Nearest Neighbors	CH 2.4-2.4.5 CH 4.1, 4.3
	02-07	Probability Review	CH 4.4-4.4.1 Blog post (s)
5	02-10	Naive Bayes	CH 4.4
	02-12	Naive Bayes			PA1
	02-14	Exam Review
6	02-17	EXAM 1
	02-19	Linear Algebra Activity	Linear Algebra Review Section 1-3.7 (skip 3.6) Video More Videos (s)
	02-21	NO CLASS
7	02-24	Gradient Descent Gradient descent activity	https://www.deeplearningbook.org Section 4.3.0 Videos Calculus Refresher (s) 9-14
	02-26	Logistic Regression	CH 4.6
	02-28	Multilayer Neural Nets	CH 4.7
8	03-02	TensorFlow Lab	https://www.deeplearningbook.org 8.1.3	PA2
	03-04	cifar_challenge.py	Keras Intro Tutorial Keras Validation Tutorial Keras Save/Load Tutorial
	03-06	Regularization nn_summary.pdf	CH 4.8 https://www.deeplearningbook.org 7 - 7.1.2, 7.4 7.12 (s) 8.2.2 - 8.2.4, 8.3.2 8.5 (s)		Poster Proposal
9	03-09	SPRING BREAK
	03-11	SPRING BREAK
	03-13	SPRING BREAK
10	03-16	COVID BREAK
	03-18	COVID BREAK
	03-20	COVID BREAK
11	03-23	Class Imbalance	CH 4.11
	03-25	Backpropagation	Review 4.7.2
	03-27	Automatic Differentiation exercise (.pdf)	Video
12	03-30	Convolutional Neural Networks CNN Activity (.pdf)	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)		PA2
	04-01	RNN Activity Do it in: Google Colab	"Unreasonable Effectiveness" tutorial Tensorflow implementation (s) https://www.deeplearningbook.org 10-10.2.0 (s)
	04-03	Word Embeddings	Word Embedding Tutorial
13	04-06	Support Vector Machines	CH 4.9	PA3
	04-08	SVM Activity
	04-10	Exam Review			Poster Bibliography
14	04-13	Exam 2
	04-15	PCA Activity	Covariance Tutorial Video Appendix B.1.1
	04-17	Nonlinear Dimensionality Reduction	PDSH: Manifold Learning
15	04-20	K-Means Activity	CH 7-7.2.2 CH 7.3
	04-22	clustering.pdf Clustering.tex	CH 7.4 CH 7.5 (skim) CH 8.1
	04-24	Work Day			Poster Submission
16	04-27	Outlier Detection	CH 9-9.3.1, 9.4 Skim 9.5-end		PA3
	04-29	FINAL EXAM REVIEW			Video Submission
	05/06/18	Final Exam 1:00-3:00PM