Prerequisite: background in algorithms, linear algebra, calculus, probability, and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156a or instructorâ€™s permission)
This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. This course will also cover core foundational concepts underpinning and motivating modern machine learning and data mining approaches. This course will also cover some recent research developments.
Assignments will be due at 9pm on Wednesday Friday via Gradscope. Students are allowed to use up to 48 late hours. Late hours must be used in units of hours. Specify the number of hours used when turning in the assignment. Late hours cannot be used on the final exam. There will be no TA support over the weekends.
Detailed policy available here
TLDR;
Yisong Yue yyue@caltech.edu
Matthew Levine  mlevine@caltech.edu 
Alex Cui  acui@caltech.edu 
James Deacon  jdeacon@caltech.edu 
Alex Guerra  aguerra@caltech.edu 
Alice Jin  qjin@caltech.edu 
Frank Kou  fkou@caltech.edu 
Marcus DominguezKuhne  mddoming@caltech.edu 
Karthik Nair  knair@caltech.edu 
Jessica Wang  jessicawang@caltech.edu 
Sherry Wang  shuxian@caltech.edu 
Erika Shuyue Yu  syu5@caltech.edu 
Albert Zhai  albertz@caltech.edu 
Jim Zhang  jim@caltech.edu 
Eric Zhao  elzhao@caltech.edu 
Note: schedule is subject to change.
Further Reading:  
1/07/2020  Lecture:  Administrivia, Basics, Bias/Variance, Overfitting  [slides]  
1/09/2020  Lecture:  Perceptron, Gradient Descent  [slides]  Daume Chapter 3 Mistake Bounds for Perceptron [link] Stochastic Gradient Descent Tricks [link] Bubeck Chaper 3 
1/09/2020  Recitation:  Introduction to Python for Machine Learning  [materials]  
1/14/2020  Lecture:  SVMs, Logistic Regression, Neural Nets, Loss Functions, Evaluation Metrics  [slides]  Bounds on Error Expectation for SVMs [link] 
1/16/2020  Lecture:  NO LECTURE  
1/16/2020  Recitation:  Linear Algebra  [slides]  The Matrix Cookbook [link] 
1/21/2020  Lecture:  Regularization, Lasso  [slides]  Murphy 13.3 
1/23/2020  Lecture:  Decision Trees, Bagging, Random Forests  [slides]  Overview of Decision Trees [pdf] Overview of Bagging [pdf] Overview of Random Forests [pdf] 
1/28/2020  Lecture:  Boosting, Ensemble Selection  [slides]  Schapire's Overview of Boosting [pdf] 
1/30/2020  Lecture:  Deep Learning  [slides]  Deep Learning Book [html] 
1/30/2020  Recitation:  PyTorch Tutorial  [slides]  
2/04/2020  Lecture:  Deep Learning Part 2  [slides]  
2/06/2020  Lecture:  Unsupervised Learning, Clustering, Dimensionality Reduction  [slides]  
2/11/2020  Lecture:  Latent Factor Models, NonNegative Matrix Factorization  [slides]  Original Netflix Paper [link] 
2/13/2020  Embeddings  [slides]  Locally Linear Embedding [link] Playlist Embedding [link] word2vec [link] Visual Style [link] 

2/18/2020  Lecture:  Probabilistic Models, Naive Bayes  [slides]  Murphy 3.5 
2/20/2020  Lecture:  NO LECTURE  
2/20/2020  Recitation:  Probability & Sampling  [slides]  
2/25/2020  Lecture:  Hidden Markov Models  [slides]  Murphy 17.317.5 
2/27/2020  Lecture:  Hidden Markov Models Part 2  (same as previous)  
2/27/2020  Recitation:  Dynamic Programming  [slides]  
3/03/2020  Lecture:  Deep Generative Models  [slides]  CS 159 (Spring 2019) [link] 
3/05/2020  Lecture:  Generative Adversarial Networks  (slide materials available after lecture)  
3/10/2020  Lecture:  NO LECTURE 