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 via Moodle. 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
Natalie Bernat  nbernat@caltech.edu 
Nishanth Bhaskara  nbhaskar@caltech.edu 
Julia Deacon  jcdeacon@caltech.edu 
Marcus DominguezKuhne  mddoming@caltech.edu 
Rupesh Jeyaram  rjeyaram@caltech.edu 
Cherie Jia  cjia@caltech.edu 
Karthik Karnik  kkarnik@caltech.edu 
Meera Krishnamoorthy  mkrishna@caltech.edu 
Karthik Nair  knair@caltech.edu 
Kapil Sinha  ksinha@caltech.edu 
Vaishnavi Shrivastava  vshrivas@caltech.edu 
Albet Tseng  atseng@caltech.edu 
Michelle Zhao  mzhao@caltech.edu 
Note: schedule is subject to change.
Further Reading:  
1/08/2019  Lecture:  Administrivia, Basics, Bias/Variance, Overfitting  [slides]  
1/10/2019  Lecture:  Perceptron, Gradient Descent  [slides]  Daume Chapter 3 Mistake Bounds for Perceptron [link] AdaGrad [link] Stochastic Gradient Descent Tricks [link] Bubeck Chaper 3 

1/10/2019  Recitation:  Introduction to Python for Machine Learning  
1/15/2019  Lecture:  SVMs, Logistic Regression, Neural Nets, Loss Functions, Evaluation Metrics  [slides]  Bounds on Error Expectation for SVMs [link]  
1/17/2019  Lecture:  Regularization, Lasso  [slides]  Murphy 13.3  
1/17/2019  Recitation:  Linear Algebra  The Matrix Cookbook [link]  
1/22/2019  Lecture:  Decision Trees, Bagging, Random Forests  [slides]  Overview of Decision Trees [pdf] Overview of Bagging [pdf] Overview of Random Forests [pdf] 

1/24/2019  Lecture:  Boosting, Ensemble Selection  [slides]  Schapire's Overview of Boosting [pdf] Papers on Ensemble Selection. [paper1][paper2] 

1/29/2019  Lecture:  Deep Learning  [slides]  Deep Learning Book [html] 

1/31/2019  Lecture:  Deep Learning Part 2  [slides]  
1/31/2019  Recitation:  Keras Tutorial  [link]  
2/5/2019  Lecture:  Unsupervised Learning, Clustering, Dimensionality Reduction  [slides]  
2/7/2019  Lecture:  Latent Factor Models, NonNegative Matrix Factorization  [slides]  Original Netflix Paper [link]  
2/12/2019  Lecture:  Embeddings  [slides]  Locally Linear Embedding [link] Playlist Embedding [link] word2vec [link] 

2/14/2019  NO LECTURE  
2/19/2019  Lecture:  Recent Applications  [slides]  
2/21/2019  Lecture:  Probabilistic Models, Naive Bayes  [slides]  Murphy 3.5  
2/21/2019  Recitation:  Probability & Sampling  
2/26/2019  Lecture:  Hidden Markov Models  [slides][notes]  Murphy 17.317.5  
2/28/2019  Lecture:  Hidden Markov Models Part 2  (same material as above)  
2/28/2019  Recitation:  Dynamic Programming  
3/5/2019  Lecture:  Recent Applications: Deep Generative Models  [slides]  
3/7/2019  Lecture:  Survey of Advanced Topics  [slides]  
3/12/2019  Lecture:  Review & Q/A 