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

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd Edition, Aurélien Géron, 2022.

Readings followed by (S) are supplemental. You may find them helpful.

WEEK	DATE	TOPIC	READING/VIDEOS	OUT	IN
1	08-21	Introduction to ML decision_trees_p_1_2.pdf	CH 1 through "Types of Machine Learning Systems"	Python Setup
2	08-26	Decision Tree Activity (.pdf)	CH 6 Fast Numpy Video https://scipy-lectures.org/ (1.3.1-1.3.2)
	08-28	Numpy Lab		PA1
3	09-02	Expectation Bias/Variance Trade-off Activity	Expected Value Bias/Variance
	09-04	Testing And Validation Activity	CH 1 "Main Challenges of Machine Learning" to end Cross Validation (3.1) Leave One Out
4	09-09	Evaluating Classifiers	CH 3 through "Error Analysis"
	09-11	EXAM 1
5	09-16	Bagging Random Forest random_forest_quiz.pdf Ada Boost Activity (pdf)	CH 7 through "AdaBoost"
	09-18	Probability Intro Probability Activity			PA1
6	09-23	Naive Bayes linear_algebra_exercises.pdf	Linear Algebra Review (S) Video (S) More Videos (S)	PA2
	09-25	Gradient Descent linear_regression.pdf linear_regression.py X_mpg.npy y_mpg.npy	Calculus Refresher (S) 9-14 Neural Net Video 1/2 Neural Net Video 2/2 CH 4 through "Mini-Batch Gradient Descent"
7	09-30	Logistic Regression	CH 4 "Logistic Regression" to end
	10-02	MLP	CH 10 to "Implementing MLPs with Keras" 3Blue1Brown Videos 1-3 (S)
8	10-07	pytorch_intro.zip PyTorch Practice	PyTorch Tensors PyTorch Autograd PyTorch Tutorial	Poster Project
	10-09	EXAM 2
9	10-14	Regularization	CH 4 "Polynomial Regression" Through "Early Stopping" CH 10 "Number of Hidden Layers" to end CH 11 Full chapter
	10-16	Autodiff	Appendix B Autodiff Video
10	10-21	FALL BREAK
	10-23	Convnets	https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s)		Poster Proposal
11	10-28	RNN	Word Embedding Tutorial "Unreasonable Effectiveness" deeplearningbook.org 10-10.2.0 (s)
	10-30	RNN	LSTM Tutorial Transformer Tutorial
12	11-04	EXAM 3
	11-06	Transformers	ThreeBlueOneBrown 5 ThreeBlueOneBrown 6 ThreeBlueOneBrown 7		Poster Bibliography
13	11-11	Transformers
	11-13	SVM	CH 4.9
14	11-18	PCA	Covariance Tutorial Video
	11-20	Nonlinear Dimensionality Reduction t-SNE Paper	PDSH: Manifold Learning		Poster Draft
15	11-25	THANKSGIVING
	11-27	THANKSGIVING
16	12-02	Clustering
	12-04	EXAM 4