--- title: Teaching --- # Raghu Meka | [HOME](http://www.raghumeka.org) | [RESEARCH](https://raghumeka.github.io/research.html) | [TEACHING](https://raghumeka.github.io/courses.html)| | -------- | -------- | -------- | --- Here are the courses I have taught or will in the future. - [**CS181: Introduction to Theory of Computing**](https://hackmd.io/@raghum/intrototcs) [Fall 2022](https://hackmd.io/@raghum/intrototcs22), [Fall 2021](https://hackmd.io/@raghum/intrototcs21), Fall 2020 - [**CS180: Algorithms and Complexity**](https://raghumeka.github.io/CS180/index.html) Spring 2018, Winter 2017; Spring 2015 - [**CS260B: Algorithmic Machine Learning (Spring 24)**](https://hackmd.io/@raghum/algoml24) [Spring 23](https://hackmd.io/@raghum/algoml23), [Spring 2021](https://hackmd.io/rBTOJg7lSj65x3gvcMoutw), [Fall 2017](https://raghumeka.github.io/CS289ML/index.html), Fall, 2016; Winter, 2016. - [**CS289: Great Theory Hits of 21st Century**](https://hackmd.io/@raghum/greathits) [Winter 2021](https://hackmd.io/F_qqEjKqTxS3S2_JYZrurg), [Winter 2018](https://raghumeka.github.io/CS289A/index.html) - [**CS289A: Pseudorandomness and explicit constructions**](https://raghumeka.github.io/CS289PR/index.html) Spring 2017; Winter, 2016.
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Raghu Meka
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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
CS181: Introduction to Theory of Computing Fall 2023

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:

12/4/2023
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