Schedule

Announcements

This is a tentative schedule. Changes will be made as the semester progresses.

Week	Topic	Material	Reading	Assignments
Week 1: Jan 22 - Jan 25	Overview. Intro to machine learning. Python.	01 Course Overview 01 High-D Spaces 02 Matrices and Plotting	From Python to Numpy, Chapters 1 - 2 Scipy Lectures, Section 1 Deep Learning, Chapters 1 - 5.1.4
Week 2: Jan 28 - Feb 1	Fitting linear models to data as a direct matrix calculation, and incrementally using stochastic gradient descent (SGD)	03 Linear Regression	Deep Learning, Section 5.1.4 and 5.9

Week	Topic	Material	Reading	Assignments
Week 3: Feb 4 - Feb 8	Stochastic gradient descent (SGD). Ridge regression. Data partitioning. Probabilistic Linear Regression. Regression with fixed nonlinearities.	04 Linear Regression Using Stochastic Gradient Descent (SGD) 05 Linear Ridge Regression and Data Partitioning 07 Linear Regression with Fixed Nonlinear Features	Deep Learning, Section 7.3 The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016.
Week 4: Feb 11 - Feb 15	Introduction to nonlinear regression with neural networks.	08 Stochastic Gradient Descent with Parameterized Activation Function	Deep Learning, Chapter 6 (skip 6.2)	A1 Stochastic Gradient Descent for Simple Models due Tuesday, February 12, 10:00 PM. Examples of good solutions
Week 5: Feb 18 - Feb 22	More neural networks	09 Scaled Conjugate Gradient for Training Neural Networks 10 More Nonlinear Regression with Neural Networks
Week 6: Feb 25 - Mar 1	Autoencoders. Activation functions.	11 Autoencoder Neural Networks	Searching for Activation Functions, by Ramachandran, Zoph, and Le Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks, by Eger, Youssef, and Gurevych	A2 Adam vs SGD due Tuesday February 26, 10:00 PM.

Week	Topic	Material	Reading	Assignments
Week 7: Mar 4 - Mar 8	Classification. LDA and QDA. K-Nearest Neighbors.	12 Introduction to Classification 13 Gaussian Distributions	Jupyter Lab: Evolution of the Jupyter Notebook by Parul Pandey
Week 8: Mar 11 - Mar 15	Classification with Neural Networks	14 Classification with Linear Logistic Regression 15 Classification with Nonlinear Logistic Regression Using Neural Networks		A3 Neural Network Regression and Activation Functions due Friday March 15, 10:00 PM.
Mar 18 - Mar 22	Spring Break
Week 9: Mar 25 - Mar 29	Pytorch. Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits.	16 Introduction to Pytorch 17 Analysis of Neural Network Classifiers and Bottleneck Networks 18 Dealing with Time Series by Time-Embedding 19 Recurrent Neural Networks

Week	Topic	Material	Reading	Assignments
Week 10: Apr 1 - Apr 5	Convolutional Neural Networks	20 Classifying Hand-drawn Digits 21 Convolutional Neural Networks 22 Introduction to Reinforcement Learning	Reinforcement Learning: An Introduction, by Sutton and Barto, 2nd ed.
Week 11: Apr 8 - Apr 12	Reinforcement Learning. Games using Tabular Q functions.	23 Reinforcement Learning with Neural Network as Q Function		Project proposal due at 10 pm Friday evening. You are welcome to start with a copy of the linked Google Doc.
Week 12: Apr 15 - Apr 19	Reinforcement Learning using Neural Networks as Q functions.	24 Reinforcement Learning to Control a Marble 25 Reinforcement Learning for Two Player Games
Week 13: Apr 22 - Apr 26	Unsupervised Learning. Dimensionality Reduction. Clustering.	26 Linear Dimensionality Reduction 27 Examples of Linear Dimensionality Reduction 28 K-Means Clustering
Week 14: Apr 29 - May 3	Hierarchical clustering. K Nearest Neighbors Classification. Support Vector Machines.	29 Hierarchical Clustering 30 Nonparametric Classification with K Nearest Neighbors 31 Support Vector Machines

Week	Topic	Material	Reading	Assignments
Week 15: May 6 - May 10	Ensembles. Other topics.	32 Ensembles of Convolutional Neural Networks 33 Machine Learning for Brain-Computer Interfaces 34 Modeling Global Climate Change		Final Project Report due Wednesday, May 8, 10:00 PM. Here is a Project Report Example
May 13 - May 16	Final Exams