HOME RESEARCH TEACHING I am a Professor in the CS department at UCLA. I am broadly interested in complexity theory, learning and probability theory. See my publications and talks for more details. I am part of UCLA's theory group. I am pleased to have received IEEE's W. Wallace McDowell Award for 2025.  :::spoiler Contact raghum@cs.ucla.***, Engineering VI 463. :::

HOME RESEARCH TEACHING Introduction 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. You can also check last year's course notes for a more details about what's to come. Make sure you can answer the questions in Assignment 0 for yourselves before registering for the course. Coursework

Raghu Meka HOME RESEARCH TEACHING Here are the courses I have taught or will in the future. CS181: Introduction to Theory of Computing Fall 2023, Fall 2022, Fall 2021, Fall 2020 CS260B: Algorithmic Machine Learning (Spring 25)

Raghu Meka HOME RESEARCH TEACHING My main interests are in complexity theory, learning theory, algorithm design. More generally, I like probability and combinatorics related things. The best resource for up to date publications really is to look up my profile on Google scholar. You can find a description of some of my recent research work in Quanta article, Science News and in this video highlight.

HOME RESEARCH TEACHING Introduction 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. You can also check last year's course notes for a more details about what's to come. Make sure you can answer the questions in Assignment 0 for yourselves before registering for the course. Coursework

HOME RESEARCH TEACHING Introduction 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. You can also check last year's course notes for a more details about what's to come. Make sure you can answer the questions in Assignment 0 for yourselves before registering for the course. Coursework

HOME RESEARCH TEACHING Introduction The course is for introducing you to the beautiful and profundly impactful world of Theory of Computing (TOC). The following three questions encompass our main learning goals: :::spoiler 1. What is computation? How different is the iMac Pro really from Apple III? :::

CS181: Introduction to Theory of Computing Fall 2022 HOME RESEARCH TEACHING Introduction The official title for the course is Formal Languages and Automata Theory. This offering will be geared towards introducing you to the beautiful and profundly impactful world of Theoretical Computer Science (TOC). The following three questions encompass our main learning goals: :::spoiler 1. What is computation? How different is the iMac Pro really from Apple III?

Raghu Meka: CS289: Great Theory Hits of 21st Century Winter 2023 HOME RESEARCH TEACHING Introduction We will cover a handful of groundbreaking results across the entire gamut of theoretical computer science discovered in the 21st century! A tentative list of topics can be found below; the topics may change based on student interest as well. 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. Check your prerequisites by testing yourself with these problems assignment. You can also check last years' notes to get an idea.

HOME RESEARCH TEACHING Introduction 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. Coursework There will be four assignments for the course that will cover 70% of the credit and a final that will cover 25% of the credit; 5% is for class participation (live-class participation and/or extensive contributions to online discussions). Each assignment will have both written as well as programming components. We'll predominantly use gradescope for the assignments. The final will be online on gradescope (and will be released as per university schedule). You could potentially discuss how to solve problems with others specifically (a better option would be to just ask questions openly on edStem) but see course policies below and make sure you do not violate them.

HOME RESEARCH TEACHING Introduction 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. Coursework There will be four assignments for the course that will cover 70% of the credit and a final that will cover 25% of the credit; 5% is for class participation (live-class participation and/or extensive contributions to online discussions). Each assignment will have both written as well as programming components. We'll predominantly use gradescope for the assignments. The final will be as per university schedule. You could potentially discuss how to solve problems with others specifically (a better option would be to just ask questions openly on edStem) but see course policies below and make sure you do not violate them.

Raghu Meka: CS289: Great Theory Hits of 21st Century Winter 2021 HOME RESEARCH TEACHING Introduction We will cover a handful of groundbreaking results across the entire gamut of theoretical computer science discovered in the 21st century! A tentative list of topics can be found below; the topics may change based on student interest as well. 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.

Raghu Meka: CS181: Introduction to Theoretical Computer Science Fall 2021 HOME RESEARCH TEACHING Introduction The official title for the course is Formal Languages and Automata Theory. This offering will be geared towards introducing you to the beautiful and profundly impactful world of Theoretical Computer Science (TOC). The following three questions encompass our main learning goals: :::spoiler 1. What is computation?

Raghu Meka: CS181: Introduction to Theoretical Computer Science, Fall 2020 HOME RESEARCH TEACHING Introduction The official title for the course is Formal Languages and Automata Theory. This offering will be geared towards introducing you to the beautiful and profundly impactful world of Theoretical Computer Science (TOC). The following three questions encompass our main learning goals: :::spoiler 1. What is computation?

If you plan to take the WQE in Fall 2020, please read the following instructions carefully. Failure to follow these instructions will result in the rejection of your WQE paper without review. Steps for submitting your paper: Create an account and register your paper at https://heron.cs.ucla.edu/wqe-f20/ by November 1st, 11:59AM Pacific time. After creating the account, start the paper registration by clicking on "New submission". Enter the paper title, the author(s) information, the abstract, and PC conflicts. You do not have to submit the PDF file at this time. Click "Save draft" to register your paper. Note that in the "PC conflicts" item, you can only select your faculty advisor(s) and faculty co-authors of this WQE paper (if they are not on the list, don't worry about it). Fill in a cover page, which must be signed (digital signature is fine) by your advisor, and submit

Welcome to Approx-Random 2020! The virtual conference to be held August 17-19 has three components: A short 10 minute live presentation/Q&A on each paper. This will be the main substitute for the live conference. These will be over zoom and the schedule is below. For Approx sessions use Zoom Room A; For Random sessions use Zoom Room B. Recorded 25 minute talks for each accepted paper available on youtube. (Linked to in the schedule below; thanks to Eshan Chattopadhyay for maintaining this). Invited talks and a gather.town event on August 18th. Please follow this link. The proceedings of the conference are available freely via LIPICS. Here you will find the detailed schedule for talks from RANDOM. The schedule for the sister conference APPROX is here.