COMS 4771 Section 2 Fall-B 2020 (Machine Learning)

Online resources for course prerequisites

If you are unsure about whether you satisfy the prerequisites for this course (or would like to “page-in” this knowledge), please check the following links.

• Multivariate calculus: videos by 3Blue1Brown; textbook by Marsden and Weinstein; MIT open courseware.
• Linear algebra: videos by 3Blue1Brown; immersive linear algebra; lecture notes from UC Davis; MIT open courseware; book chapter by Goodfellow, Bengio, and Courville; videos by Ule von Luxburg; additional review notes
• Probability: textbook by Grinstead and Snell; book chapter by Goodfellow, Bengio, and Courville; review notes by Arian Maleki and Tom Do
• Algorithms: Chapter 0 of textbook by Dasgupta, Papadimitriou, and Vazirani (with discussion of asymptotic notation); “booksite” by Sedgewick and Wayne
• Mathematical maturity: notes on writing mathematics well from HMC; notes on writing math in paragraph style from SJSU; notes on writing proofs from SJSU.

Topics

• Overview of machine learning
• Nearest neighbors
• Prediction theory
• Regression I: Linear regression
• Regression II: Regularization
• Multivariate Gaussians and PCA
• Regression III: Kernels
• Classification I: Linear classification
• Optimization I: Convex optimization
• Classification II: Margins and SVMs
• Classification III: Classification objectives
• Optimization II: Neural networks

If time permits, we may also cover other topics such as boosting, unsupervised learning, online decision making (depending on student interest).

Readings

The lectures will be mostly self-contained, but required reading assignments (which should be completed prior to lecture) will be posted on the website. Additional reading material from some of the following texts will be suggested.

• Machine Learning: A Probabilistic Perspective (MLPP) by Murphy
• Pattern Classification (PC) by Duda, Hart, and Stork
• A Course in Machine Learning (CML) by Daumé
• Mathematics for Machine Learning (MML) by Deisenroth, Faisal, and Ong
• The Elements of Statistical Learning (ESL) by Hastie, Tibshirani, and Friedman

(All of these texts are available online, possibly through Columbia University Libraries.)

Assignments

The overal course grade is comprised of:

• homework assignments (40%)
• quizzes (30%)
• final exam (30%); projected to be Tuesday, December 22

Please submit all assignments by the specified due dates. Extensions are generally only granted for medical reasons. (Please ask your academic advisor to confirm documentation from a physician / medical practitioner, and then ask them to email me their confirmation.)

All written portions of assignments should be neatly typeset as PDF documents. This will make grading much easier! You can use LaTeX, Microsoft Word, or any other system that produces high-quality PDFs with neatly typeset equations. If you have not used LaTeX before, or if you only have a passing familiarity with it, it is recommended that you read and complete the lessons and exercises in The Bates LaTeX Manual or on learnlatex.org. This video by Ryan O’Donnell on writing math in LaTeX is also recommended.

Collaboration on homework assignments

You are welcome and encouraged to discuss homework assignments with fellow students. Your discussions should respect the following rules.

• Homework assignments should be completed individually or in groups of at most three students (including yourself).
  • We will provide instructions for submitting assignments as a group.
  • Every group member must contribute to every part of the assignment; no one should be just “along for the ride”.
  • Every group member must take responsibility for the entire submitted write-up.
  • The submitted write-up should be completely in your own words. If you need to quote or reference a source, you must include proper citations in your write-up.
• Discussion between groups may include brainstorming and verbally discussing possible solution approaches, but must not go as far as one person telling another how to solve the problem.
  • You may not take any notes (whether handwritten or typeset) from the discussions.
  • Any written/electronic discussions (e.g., over messaging platforms, email) should be discarded/deleted immediately after they take place.
• You may not look at another group’s homework write-up/solutions (whether partial or complete).
• You may not show your homework write-up/solutions (whether partial or complete) to another group.

Collaboration or discussion between students is NOT PERMITTED on quizzes or exams.

Use of outside references on homework assignments

Outside reference materials and sources (i.e., texts and sources beyond the assigned reading materials for the course) may be used on homework only if given explicit written permission from the instructor and if the following rules are followed.

• Any outside reference must be acknowledged and cited in the write-up.
• Sources obtained by searching the literature/internet for answers or hints on homework assignments are never permitted.
• You are permitted to use texts and sources on course prerequisites (e.g., a linear algebra textbook).
  • If you need to look up a result in such a source, provide a citation in your homework write-up.
• If you inadvertently come across a solution to (or substantial hint about) a problem, you must:
  • acknowledge this source and document the circumstance in your homework write-up;
  • produce a solution without looking at the source; and
  • as always, write your solution in your own words.
• If you have already seen one of the homework problems before (e.g., in a different course), please re-solve the problem without referring to any previous solutions.
  • In your write-up, please also indicate that you had seen the problem before. (You won’t lose any credit for this; it would just be helpful for us to know about this fact.)

Outside references CANNOT be used on quizzes or exams unless you have received explicit written permission from the instructor.

Violations

Violation of any portion of these policies will result in a penalty to be assessed at the instructor’s discretion (e.g., a zero grade for the assignment in question, a failing letter grade for the course). All violations are reported to the relevant dean’s office.