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. Coursework5/22/2023
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. Publications On User-Level Private Convex Optimization ICML 20235/16/2023
Raghu Meka HOME RESEARCH TEACHING Here are the courses I have taught or will in the future. CS181: Introduction to Formal Languages and Automata Theory Fall 2022, Fall 2021, Fall 2020 CS180: Algorithms and Complexity3/27/2023
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.3/1/2023
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