In this course we will look at a handful of ubiquitous algorithms in machine learning. We will cover several classical tools in machine learning but more emphasis will be given to recent advances and developing efficient and provable algorithms for learning tasks. A tentative syllabus/schedule can be found below; the topics may change based on student interests as well.

Class transcript: Keep in mind that the transcript only shows what is actually written in class and are not complete notes as such.

Lecture notes for multiplicative weights method (lectures 3,4).

Lecture notes for gradient descent (lectures 8,9).

We will use Piazza for the class. Class page is here. You can singup here.

Assignment 1. Due October 18th, 10PM via Gradescope.

Assignment 2. Due November 8th, 10PM via Gradescope.

Assigment 3. Due November 21st, 10PM via Gradescope.

Assignment 4. Due December 11, 10PM via Gradescope.

Prerequisites: You need background in linear algebra, probability theory, and algorithms (all at a typical undergraduate upper-division level) to make the class fun and interesting for you as well as for me. You can judge your background with this problem set (not to be submitted). If you have doubts about this please talk to me right away.

Course work: We will have four assignments: 1,2,3 - 16; 4 - 20.

Mid-term - 32. In class on November 20. There will be no make up exams for the class; you have to let me know in the first two weeks if the date is an issue for you.

Optional project: If you so desire, you can substitute the last assignment for an approved project. This can either be a cohesive literature survey of a specific topic, a research project, or an experimental project investigating different algorithms on a specific learning problem; it can even be in the form of participating in some machine learning competitions. Projects finding connections between the course material and their own research interests are strongly encouraged.
The project topic has to be finalized before Nov 29th and will be evaluated on the basis of a five page (one-sided) report (Due by December 14th 5PM PST) which is expected to be at the level of a conference submission. The project can be done in teams of up to two students (the work will have to scale accordingly).

Assignment submission: We will use Gradescope for assignments and they have to be submitted by 10PM on their due date. This is extremely helpful both for me as well as for you - you'll get better feedback and will have a digital record of all your assignments that you can refer to later. Things to keep in mind: 1) Within two weeks of the course, you should receive a registration link from Gradescope. If you don't receive it before the first homework, contact me immediately; this will give you access to the website. 2) Watch this one-minute video with complete instructions. Follow them to the letter! The simple guidelines make the process considerably smoother. 3) Make sure you start each problem of an assignment on a new page. 4) To generate a PDF scan of the assignments, you can follow the instructions here; you can also use the scanners in the library.

Resources: There is no required course text. The following links would be useful:
Sanjeev Arora's course.
Elad Hazan's course.
Ankur Moitra's course.
Draft of Foundations of Data Science by Blum, Hopcroft and Kannan.
Amit Chakrabarti's lecture notes on streaming algorithms.
Links to appropriate papers or other online material (typically other lecture notes) will be provided for each lecture.

Hours & Location: MW 2-3:50, Boelter Hall 5272. Office hours: Wednesday 10:30 - 11:30, BH 3732H.

The following is a tentative list of topics to be covered.
Learning theory: what and how? (2 lectures)
How to model learning?
PAC model and Generalization
Towards tractable learning models
Linearity: the swiss-army knife (3 lectures)
Best-fit subspaces, low-rank approximations, and Singular Value Decomposition
Applications of SVD
Multiplicative weights and online learning (2 lectures)
Online optimization and regret minimization.
Optimization: the work-horse of learning (3 lectures)
Convexity primer
Learning as optimization
Gradient descent
Stochastic gradient descent
Accelrated gradient descent methods
Regression and its analysis (2 lectures)
How and when to regress?
Graphical Models (3 lectures)
Basic models and algorithms
Neural networks (2 lectures)
Constant-depth circuits, back propogation, and limitations
The reemergence of neural nets
Algorithms for massive data sets (2 lectures)
Streaming algorithms
Miscellaneous/buffer (1 lecture)

Academic honesty: The students are expected to fully abide by UCLA's student conduct policies, including Section 102.01 on academic honesty. You will find a wealth of helpful materials here, including the Student Guide to Academic Integrity. Academic dishonesty will be promptly reported to the Dean of Students' Office for adjudication and disciplinary action. Remember, cheating will have significant and irrevocable consequences for your academic record and professional future. Please don't cheat.

While collaboration with other students on assignments is fine, you should clearly mention the collaborators. You should make your own slides and when you use content from another source, you should explicitly state so. Under no circumstances may you use code directly from resources on the web without explicitly citing the source.