--- title: Great Theory Hits --- # Raghu Meka: CS289: Great Theory Hits of 21st Century Winter 2023 | [HOME](http://www.raghumeka.org) | [RESEARCH](http://raghumeka.github.io/research.html) | [TEACHING](http://raghumeka.github.io/courses.html) | | -------- | -------- | -------- | --- ## 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](https://ucla.box.com/s/zra5139k5625a68g09anv7jkfjnc7y4r). You can also check last years' [notes](https://hackmd.io/F_qqEjKqTxS3S2_JYZrurg) to get an idea. Undergrads are welcome! I will allow PTEs as long as you can assure yourself that you have the prerequisites. If in doubt, please contact me by email. **Lectures: M-W 10-11:50. BOELTER 5422**. Office hours are by appointment (quite flexible). [Zoom link](https://ucla.zoom.us/j/93361419541). (Zoom interactions won't count toward class participation.) **edStem:** We will use edStem for communication and announcements (including for links to video recordings) - please register [here](https://edstem.org/us/join/V6qgdE). --- ## Course Work We will have three take-home midterms worth 30% each. - Midterm 1: February 1. Due in 48 hours. - Midterm 2: February 27. Due in 48 hours. - Midterm 3: March 20th. Due in 48 hours. 10% credit is for class participation (asking questions in class). If you can't attend class live, then talk to me and we will figure out a suitable fix. The best resources will be the original papers. The transcipt of the lectures will be available on Scribble. You can also access last year's notes (and other details) on previous years' course webpage [here](https://hackmd.io/F_qqEjKqTxS3S2_JYZrurg). --- ## Notes :::spoiler **Course notes**: We will use [ScribbleTogether](https://scribbletogether.com/). - Note that each lecture is on a different sheet within the files. - [Notes on extension complexity](https://scribbletogether.com/whiteboard/6C452FDF-4AE9-47C4-9589-8431B27B39EA) - [Notes on information complexity](https://scribbletogether.com/whiteboard/9E530A3D-95EC-4C29-A34C-C9676BD36FFE) - [Notes for Sunflower](https://scribbletogether.com/whiteboard/A355971E-3A0D-4776-9E0B-DD9A81B7E5D7). - [Notes for Parts 1, 2](https://scribbletogether.com/whiteboard/7BD725CA-A417-4CD0-92DE-78C3304FC58B). --- ## Syllabus The syllabus is flexible and we can cover diverse topics based on your interest. The following is a tentative list of topics to be covered. For each of the specific results, we will use it as an excuse to learn some essential tools in theory of computing. - SL = L: Undirected connectivity in Logspace (4 lectures). Refs: [This chapter](https://people.seas.harvard.edu/~salil/pseudorandomness/expanders.pdf) and [the original paper](https://omereingold.files.wordpress.com/2014/10/sl.pdf). This excellent (and award winning) survey on [expander graphs](https://www.ams.org/journals/bull/2006-43-04/S0273-0979-06-01126-8/) for further topics. - Sensitivity conjecture (2 lectures) - Improved bounds on sunflower conjecture (3 lectures) - Information Complexity and Applications (4 lectures) - Quantum LDPC Codes (5 lectures)
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Raghu Meka
4069

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Algorithmic Machine Learning

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.

4/24/2024
Research

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.

2/29/2024
Algorithmic Machine Learning

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.

2/23/2024
Teaching

Here are the courses I have taught or will in the future.

2/23/2024
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