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Schedule

Announcements

Lecture videos will be available.

Week	Topic	Material	Reading	Assignments
Week 1: Jan 17 - Jan 20	Overview. Intro to machine learning. Python.	01 Course Overview, 02 Matrices and Plotting,	The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016. Section 1 of Scipy Lecture Notes
Week 2: Jan 23 - Jan 27	Probability distributions and regression.	03 Linear Regression, 04 Gaussian Distributions

Week	Topic	Material	Assignments
Week 3: Jan 30 - Feb 3	Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression.	05 Fitting Gaussians, 06 Probabilistic Linear Regression, 07 Linear Ridge Regression and Data Partitioning, 08 Sample-by-Sample Linear Regression	A1 Linear Regression due Monday, January 30th at 10:00 PM.
Week 4: Feb 6 - Feb 10	Regression with fixed nonlinearities. Nonlinear regression with neural networks. Feb 10: Guest Speaker Mike Morain, Machine Learning at Amazon, UK.	09 Linear Regression with Fixed Nonlinear Features, 10 Nonlinear Regression with Neural Networks
Week 5: Feb 13 - Feb 17	Neural Networks	10 Nonlinear Regression with Neural Networks, 11 More Nonlinear Regression with Neural Networks	A2 Ridge Regression with K-Fold Cross-Validation due Monday, February 13th at 10:00 PM. Here are examples of good A2 reports.
Week 6: Feb 20 - Feb 24	Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee.	12 Autoencoder Neural Networks
Week 7: Feb 27 - Mar 3	Recurrent Neural Networks. Conditional probabilities and Bayes Rule	13 Recurrent Neural Networks 14 Introduction to Classification	A3 Neural Network Regression due Wednesday, March 1st at 10:00 PM. Here are examples of good A3 reports.

Week	Topic	Material	Reading
Week 8: Mar 6 - Mar 10	Classification. LDA and QDA. Linear and Nonlinear Logistic Regression.	15 Classification with Linear Logistic Regression 16 Classification with Nonlinear Logistic Regression Using Neural Networks
Week 9: Mar 20, Mar 24 No class March 22nd.	Classification. Analysis of Trained Networks. Bottleneck Networks. Hand-Drawn Digit Classification.	17 Analysis of Neural Network Classifiers and Bottleneck Networks 18 Digits
Week 10: Mar 27 - Mar 31	Convolutional Neural Networks. Reinforcement Learning.	19 Convolutional Neural Networks 20 Introduction to Reinforcement Learning	Reinforcement Learning: An Introduction, by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free.

Week	Topic	Material	Reading	Assignments
Week 11: Apr 3 - Apr 7	Reinforcement Learning. Two-player games.	21 Reinforcement Learning for Two Player Games	Reinforcement Learning: An Introduction, by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free.	A4 Classification with LDA and Logistic Regression due Wednesday, April 5th at 10:00 PM. Here are examples of good A4 reports. Project Proposal due Friday, April 7th at 10:00 PM.
Week 12: Apr 10 - Apr 14	Neural networks as Q functions.	22 Reinforcement Learning with Neural Network as Q Function Faster RL by Pre-training	The Dark Secret at the Heart of AI The Tiny Changes That Can Cause AI to Fail
Week 13: Apr 17 - Apr 21	Unsupervised Learning. Dimensionality reduction.	23 Linear Dimensionality Reduction 24 Nonlinear Dimensionality Reduction with Digits Example 25 K-Means Clustering 26 Hierarchical Clustering
Week 14: Apr 24 - Apr 28	Nonparametric Classification Algorithms	27 Nonparametric Classification with K Nearest Neighbors 28 Support Vector Machines		A5 Control a Marble with Reinforcement Learning due Monday, April 24th at 10:00 PM. Here are examples of good A5 reports.

Week	Topic	Material	Reading	Assignments
Week 15: May 1 - May 5	Brain-Computer Interfaces. Ensembles.	29 Machine Learning for Brain-Computer Interfaces 30 Comparison of Algorithms for BCI 31 Convolutional Neural Networks for BCI 32 Ensembles of Convolutional Neural Networks Patterns in EEG for Brain-Computer Interfaces and Recent Results with Tripolar EEG Electrodes		Please complete the Course Surveys that are now available on Canvas. Fill out the survey for your section, either on-campus or distance-learning.
Finals Week: May 8 - May 11				Final project due Tuesday, May 9, 10:00 PM. Here is a summary of what is expected in your reports.