(CS/CNS/EE 155) Machine Learning & Data Mining


2017/2018 Winter Term (previous year)

Course Description

Prerequisite: background in algorithms, linear algebra, calculus, probability, and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156a or instructor’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.

Course Details

Late Homework Policy

Assignments will be due at 9pm on Friday 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.

Collaboration Policy

Homeworks: (taken from CS 1) It is common for students to discuss ideas for the homework assignments. When you are helping another student with their homework, you are acting as an unofficial teaching assistant, and thus must behave like one. Do not just answer the question or dictate the code to others. If you just give them your solution or code, you are violating the Honor Code. As a way of clarifying how you can help and/or discuss ideas with other students (especially when it comes to coding and proofs), we want you to obey the "50 foot rule". This rule states that your own solution should be at least 50 feet away. If you are helping another students but cannot without consulting your solution, don't help them, and refer them instead to a teaching assistant.

Miniprojects: Students are allowed to collaborate fully within their miniproject teams, but no collaboration is allowed between teams.

Final Exam: No collaboration of any kind is allowed.

Instructor

Yisong Yue               yyue@caltech.edu

Teaching Assistants

Ellen Feldman      efeldman@caltech.edu
Nishanth Bhaskara nbhaskar@caltech.edu
Rohan Choudhury rchoudhury@caltech.edu
Julia Deacon jcdeacon@caltech.edu
Katherine Guo kguo@caltech.edu
Michael Hashe mhashe@caltech.edu
Joey Hong jhhong@caltech.edu
Andrew Kang akang@caltech.edu
Catherine Ma cmma@caltech.edu
Ruoqi Shen rshen@caltech.edu
Richard Zhu lzhu@caltech.edu
Vincent Zhuang vzhuang@caltech.edu

Office Hours

Optional Textbooks

  • Machine Learning: a Probabilistic Perspective, by Kevin Murphy
  • Convex Optimization: Algorithms and Complexity (Free Version), by Sebastien Bubeck
  • A Course in Machine Learning, by Hal Daume III
  • Since this is an advanced level course, all relevant course materials can be learned via research papers and supplementary lecture notes. However, these books are excellent references and I will refer to various chapters throughout the course.

    Assignments

    Lectures & Recitation Schedule

    Note: schedule is subject to change.

                                    Further Reading:                                                
    1/04/2017 Lecture: Administrivia, Basics, Bias/Variance, Overfitting [slides]
    1/04/2017 Recitation: Introduction to Python for Machine Learning [slides][iPython]
    1/09/2017 Lecture: Perceptron, Gradient Descent [slides] Daume Chapter 3
    Mistake Bounds for Perceptron [link]
    AdaGrad [link]
    Stochastic Gradient Descent Tricks [link]
    Bubeck Chaper 3
    1/11/2017 Lecture: SVMs, Logistic Regression, Neural Nets, Loss Functions, Evaluation Metrics [slides] Bounds on Error Expectation for SVMs [link]
    1/11/2017 Recitation: Linear Algebra [slides] The Matrix Cookbook [link]
    1/16/2017 Lecture: Regularization, Lasso [slides] Murphy 13.3
    1/18/2017 Lecture: Decision Trees, Bagging, Random Forests [slides] Overview of Decision Trees [pdf]
    Overview of Bagging [pdf]
    Overview of Random Forests [pdf]
    1/23/2017 Lecture: Boosting, Ensemble Selection [slides] Shapire's Overview of Boosting [pdf]
    1/25/2017 Lecture: Deep Learning [slides] Deep Learning Book [html]
    A Brief Overview of Deep Learning. [link]
    1/25/2017 Recitation: Keras Tutorial [slides] [link]
    1/30/2017 Lecture: Deep Learning Part 2 [slides]
    2/1/2017 Lecture: Recent Applications: Edge Detection & Speech Animation [slides]
    2/6/2017 Lecture: Unsupervised Learning, Clustering, Dimensionality Reduction [slides]
    2/8/2017 Lecture: Latent Factor Models, Non-Negative Matrix Factorization [slides] Original Netflix Paper [link]
    2/13/2017 Lecture: Embeddings [slides] Locally Linear Embedding [link]
    Playlist Embedding [link]
    word2vec [link]
    2/15/2017 Lecture: Recent Applications: Representation Learning [slides] [paper 1]
    [paper 2]
    [paper 3]
    2/20/2017 Lecture: Probabilistic Models, Naive Bayes Murphy 3.5
    2/20/2017 Recitation: *TUESDAY* Probability & Sampling
    2/22/2017 Lecture: Hidden Markov Models Murphy 17.3--17.5
    2/27/2017 Lecture: Hidden Markov Models Part 2
    2/27/2017 Recitation: *TUESDAY* Dynamic Programming
    3/1/2017 Lecture: Recent Applications: Deep Generative Models
    3/6/2017 Lecture: Survey of Advanced Topics
    3/8/2017 Lecture: Review & Q/A

    Additional References