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 Complexity
3/27/2023Raghu 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.
3/1/2023HOME 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.
2/24/2023HOME 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.
2/24/2023