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Introduction to Machine Learning 10-701, Fall 2020

Foundations and Non-Parametric Methods

Date Lecture Topic Instructor Links
Mon Aug-31 Intro, Three Axes of ML: Data, Algorithms, Tasks, Intro to probability Ziv SlidesVideo
Wed Sep-02 Bayesian Estimation, MAP, MLE Ziv SlidesVideo
Fri Sep-04 Recitation 1    
Mon Sep-07 No Classes: Labor Day    
Wed Sep-09 Decision Theory, Risk Minimization, K nearest neighbors Ziv SlidesVideo
Fri Sep-11 Recitation 2    

Prediction, Parametric Methods

Date Lecture Topic Instructor Links
Mon Sep-14 Naive Bayes, Generative vs Discriminative Ziv SlidesVideo
Wed Sep-16 Decision Trees Ziv SlidesVideo
Fri Sep-18 Recitation 3    
Mon Sep-21 Bagging, Random Forest, Linear regression Ziv SlidesVideo
Wed Sep-23 Logistic Regression Ziv SlidesVideo
Fri Sep-25 Recitation 4    
Mon Sep-28 No Classes: Yom Kippur    
Wed Sep-30 Support Vector Machines 1 Ziv SlidesVideo
Fri Oct-02 Recitation 5    
Mon Oct-05 Support Vector Machines 2 Ziv SlidesVideo
Wed Oct-07 Neural Networks and Deep Learning Eric SlidesVideo
Fri Oct-09 Recitation 6    
Mon Oct-12 Neural Networks and Deep Learning 2 Eric SlidesVideo
Wed Oct-14 Boosting, Surrogate Losses, Ensemble Methods Eric SlidesVideo
Fri Oct-16 No Classes: Community Engagement day    

Unsupervised and Representation Learning

Date Lecture Topic Instructor Links
Mon Oct-19 Clustering, Kmeans Eric SlidesVideo
Wed Oct-21 Clustering: Mixture of Gaussians, Expectation Maximization Eric SlidesVideo
Fri Oct-23 No Classes: Midsemester Break    
Mon Oct-26 Representation Learning: Feature Transformation, Random Features, PCA Eric SlidesVideo
Wed Oct-28 Representation Learning: PCA, ICA Eric Slides
Fri Oct-30 Recitation 7    

Graphical and sequence models

Date Lecture Topic Instructor Links
Mon Nov-02 Graphical Models (Bayesian Networks) Ziv  
Wed Nov-04 Graphical Models (Bayesian Networks 2) Ziv  
Fri Nov-06 Recitation 8    
Mon Nov-09 Sequence Models: HMMs Ziv  
Wed Nov-11 Sequence Models: State Space Models, other time series models Ziv  
Fri Nov-13 Recitation 9    

Theoretical considerations

Date Lecture Topic Instructor Links
Mon Nov-16 Learning Theory: Statistical Guarantees for Empirical Risk Minimization Eric  
Wed Nov-18 Generalization, Model Selection Eric  
Fri Nov-20 Recitation 10 Proposed Final Exam Review    
Mon Nov-23 Exam    
Wed Nov-25 No Classes: Thanksgiving    
Fri Nov-27 No Classes: Thanksgiving    

Actions

Date Lecture Topic Instructor Links
Mon Nov-30 Industry lecture    
Wed Dec-02 Reinforcement Learning Eric  
Fri Dec-04 Recitation 11    
Mon Dec-07 Reinforcement Learning 2 Eric  
Wed Dec-09 Project presentations    
Fri Dec-11 Recitation 12