Course Overview

In this course we will introduce classical methods in pattern classification and machine learning, focusing on statistical learning approaches for supervised and unsupervised learning problems. We will present the theoretical and algorithmic underpinnings of these methods. The course will consist of biweekly lectures, problem sets that contain both mathemetical and MATLAB/Octave programming exercises, and two in-class exams.

Prerequisites

Undergraduate level training or coursework in algorithms, linear algebra, calculus and multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required. A background in programming will also be necessary for the problem sets; specifically students are expected to be familiar with MATLAB/Octave or learn it during the course.

Piazza Forum

We will use Piazza for class discussions. Please go to this Piazza website to join the course forum (note: you must use a ucla.edu email account to join the forum). We strongly encourage students to post on this forum rather than emailing the course staff directly (this will be more efficient for both students and staff). Students should use Piazza to:

• Ask clarifying questions about the course material.
• Share useful resources with classmates (so long as they do not contain homework solutions).
• Look for project partners or other students to form study groups.
• Answer questions posted by other students to solidify your own understanding of the material.

The course Academic Integrity Policy must be followed on the message boards at all times. Do not post or request homework solutions! Also, please be polite.

Staff Contact Info

Instructor: Prof. Ameet Talwalkar
Office Hours: Monday after class, Boelter 4531F
Email: ameet at cs.ucla.edu

Teaching Assistants:

• Brooke Wenig
  Office Hours: Tuesday 6-7pm and Wednesday 12-1pm, Boelter 2432
  Email: brookewenig at gmail
  
• Chris Wu
  Office Hours: Thursday 3-4pm and Friday 11a-noon, Boelter 2432
  Email: chdmwu at gmail

Textbooks

There will be no required textbooks, though we suggest the following to help you to study:

• Machine Learning: A Probabilistic Perspective by Kevin Murphy.
• Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (freely available online).

We will provide suggested readings from these books in the schedule below, using the acronyms MLAPA and ESL.

Academic Integrity Policy

Group studying and collaborating on problem sets are encouraged, as working together is a great way to understand new material. Students are free to discuss the homework problems with anyone under the following conditions:

• Students must write their own solutions and understand the solutions that they wrote down.
• Students must list the names of their collaborators (i.e., anyone with whom the assignment was discussed).
• Students may not use old solution sets from this class or any other class under any circumstances, unless the instructor grants special permission.

Students are encouraged to read the Dean of Students' guide to Academic Integrity.

Grading Policy

Grades will be based on the following components:

• Problem Sets (30%): There will be 6 problem sets. Each each problem set having equal weight.
  • Problem sets are due at the beginning of class on the due date. Late submissions will not be accepted.
  • Students can drop their lowest grade (i.e., only the top 5 grades will count).
  • All solutions must be typed (preferably using LaTeX) and printed out. Solutions that are not typed will be penalized 50%; unreadable answers will not be graded.
  • Solutions will be graded on both correctness and clarity. If you cannot solve a problem completely, you will get more partial credit by identifying the gaps in your argument than by attempting to cover them up.
  • For some assignments, it might be the case that only a subset of the problems will be graded in detail. Students may or may not be told which problems will be graded in advance.
  • Homeworks will NOT be graded unless you have taken the course Math Quiz.
• Midterm (30%), Final (40%): These in-class exams will cover material from the lectures and the problem sets.

Regrade requests for homework or exams must be made within one week after the graded assignments have been handed out, regardless of whether you attended class that day. Please put your request in writing, indicating the parts you want regraded and why, and hand in your request to the TA in person. Please keep in mind the unlikely but real possibility that a regrade may lower your grade, in case a previously overlooked mistake is discovered.

Using MATLAB / Octave

All registered students for this course can obtain MATLAB access via their SEASnet accounts. This applies to both Engineering and non-Engineering students), though students who are not officially enrolled (per the Registrar's Office) will not be able to get a SEAS account. SEASnet accounts can be applied for by going to this link, then clicking on the "Create Account" tab in the upper left hand corner. Student accounts must be picked up in person at the SEASnet Help Desk. Remote Desktop is also available to all enrolled students in Engineering. If you need assistance please contact help@seas.ucla.edu.

Octave is freely available.

Using LaTeX

Students are strongly encouraged to use LaTeX. LaTeX makes it simple to typeset mathematical equations, and is extremely useful for grad students to know. Most of the academic papers you read were written with LaTeX, and probably most of the textbooks too. Here is an excellent LaTeX tutorial and here are instructions for installing LaTeX on your machine. Also note that LaTeX is also installed on department-run linux machines.

Acknowledgments

This course is based in part on material developed by Fei Sha. Some of the administrative content on the course website is adapted from material from Jenn Wortman Vaughan, Rich Korf, and Alexander Sherstov.

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Schedule

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