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Schedule

Announcements

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

January

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

February

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
A2 Adam vs SGD
Deep Learning, Chapter 6 (skip 6.2) A1 Stochastic Gradient Descent for Simple Models due Tuesday, February 12, 10:00 PM.
Week 5:
Feb 18 - Feb 22
More 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 A2 Adam vs SGD due Monday February 25, 10:00 PM.

March

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
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
16 Introduction to Pytorch
Mar 18 - Mar 22 Spring Break
Week 9:
Mar 25 - Mar 29
Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits. 17 Analysis of Neural Network Classifiers and Bottleneck Networks
18 Dealing with Time Series by Time-Embedding
19 Recurrent Neural Networks

April

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

May

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
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