Awesome Machine Learning and AI Courses
Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.
By Lukas Spranger, Data Scientist and Software Engineer
Editor's note: Note that this is an activelygrowing collection of courses, and that you can contribute to the repository by simply opening a pull request.
A curated list of awesome, free machine learning and artificial intelligence courses with video lectures. All courses are available as highquality video lectures by some of the best AI researchers and teachers on this planet.
Besides the video lectures, I linked course websites with lecture notes, additional readings and assignments.
Introductory Lectures
These are great courses to get started in machine learning and AI. No prior experience in ML and AI is needed. You should have some knowledge of linear algebra, introductory calculus and probability. Some programming experience is also recommended.
 Machine Learning (Stanford CS229)  Course website
This modern classic of machine learning courses is a great starting point to understand the concepts and techniques of machine learning. The course covers many widely used techniques, The lecture notes are detailed and review necessary mathematical concepts.
 Convolutional Neural Networks for Visual Recognition (Stanford CS231n)  Course website
A great way to start with deep learning. The course focuses on convolutional neural networks and computer vision, but also gives an overview on recurrent networks and reinforcement learning.
 Introduction to Artificial Intelligence (UC Berkeley CS188)  Course website
Covers the whole field of AI. From search methods, game trees and machine learning to Bayesian networks and reinforcement learning.
 Applied Machine Learning 2020 (Columbia)
Alternative to Stanford CS229. As the name implies, this course takes a more applied perspective than Andrew Ng's machine learning lecture at Stanford. You will see more code than mathematics. Concepts and algorithms are using the popular Python libraries scikitlearn and Keras.
 Introduction to Reinforcement learning with David Silver (DeepMind)  Course website
Introduction to reinforcement learning by one of the leading researchers behind AlphaGo and AlphaZero.
 Natural Language Processing with Deep Learning (Stanford CS224N)  Course website
Modern NLP techniques from recurrent neural networks and word embeddings to transformers and selfattention. Covers applied topics like questions answering and text generation.
Advanced Lectures
Advanced courses that require prior knowledge in machine learning and AI.
 Deep Unsupervised Learning (UC Berkeley CS294)  Course website
 Frontiers of Deep Learning (Simons Institute)  Course website
 New Deep Learning Techniques  Course website
 Geometry of Deep Learning (Microsoft Research)  Course website
 Deep MultiTask and Meta Learning (Stanford CS330)  Course Website
 Mathematics of Machine Learning Summer School 2019 (University of Washington)  Course Website
 Probabilistic Graphical Models (Carneggie Mellon University)  Course Website
 Probabilistic and Statistical Machine Learning 2020 (University of Tübingen)
 Statistical Machine Learning 2020 (University of Tübingen)
 Mobile Sensing and Robotics 2019 (Bonn University)
 Sensors and State Estimation Course 2020 (Bonn University)
 Photogrammetry 2015 (Bonn University)
 Advanced Deep Learning & Reinforcement Learning 2020 (DeepMind / UCL)
 DataDriven Dynamical Systems with Machine Learning
 DataDriven Control with Machine Learning
 ECE AI Seminar Series 2020 (NYU)
 CS287 Advanced Robotics at UC Berkeley Fall 2019
 CSEP 546  Machine Learning (AU 2019) (U of Washington)
 Deep Reinforcment Learning, Decision Making and Control (UC Berkeley CS285)
 Stanford Convex Optimization
 Stanford CS224U: Natural Language Understanding  Spring 2019
 Full Stack Deep Learning 2019
 Emerging Challenges in Deep Learning
 DeepBayes 2019 Summer School
 CMU Neural Nets for NLP 2020
 New Directions in Reinforcement Learning and Control (Institure for Advanced Study)
 Workshop on Theory of Deep Learning: Where next (Institure for Advanced Study)
 Deep Learning: Alchemy or Science? (Institure for Advanced Study)
 Theoretical Machine Learning Lecture Series (Institure for Advanced Study)
Bio: Lukas Spranger (@sprangerlukas) is a data scientist and software engineer. Currently, he is working on datadriven and AIenabled software solutions at Siemens. He holds a Master's degree in computer science and is excited about our ability to build a better future through technology.
Original. Reposted with permission.
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