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https://github.com/tomkarsten/awesome-courses

Awesome AI/ML Courses
https://github.com/tomkarsten/awesome-courses

List: awesome-courses

awesome-list courses data-science deep-learning machine-learning natural-language-processing reinforcement-learning

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Awesome AI/ML Courses

Host: GitHub
URL: https://github.com/tomkarsten/awesome-courses
Owner: tomkarsten
Created: 2022-10-26T07:27:53.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2023-05-29T17:55:09.000Z (over 1 year ago)
Last Synced: 2024-11-26T00:01:59.813Z (26 days ago)
Topics: awesome-list, courses, data-science, deep-learning, machine-learning, natural-language-processing, reinforcement-learning
Homepage:
Size: 14.6 KB
Stars: 4
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-courses - Awesome AI/ML Courses. (Other Lists / Monkey C Lists)

README

        # 🎓 Awesome AI/ML Courses



## Deep Learning

* [DeepMind -**The Deep Learning Lecture Series**](https://deepmind.com/learning-resources/deep-learning-lecture-series-2020)

  * Convolutional Neural Networks | Advanced Models for Computer Vision | Optimisation for Machine Learning | Sequences and Recurrent Networks | Deep Learning for Natural Language Processing | Attention and Memory in Deep Learning | Generative Adversarial Networks | Unsupervised Representation Learning | Modern Latent Variable Models | Responsible Innovation & Artificial Intelligence

  * [:tv: Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) 

  * [:open_book: Slides/Lecture Notes](https://deepmind.com/learning-resources/deep-learning-lecture-series-2020)

* [NYI Center for Data Science - **Yann LeCun’s Deep Learning Course**](https://cds.nyu.edu/deep-learning/)

  * Parameters sharing Energy based models | Associative Memories | Graphs | Control | Optimization

  * [:tv: Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) 

  * [:open_book: Slides/Lecture Notes](https://atcold.github.io/pytorch-Deep-Learning/)

  * [:memo: Notebooks](https://github.com/Atcold/pytorch-Deep-Learning)

* [Université de Liège - **Deep Learning (INFO8010)**](https://github.com/glouppe/info8010-deep-learning)  

  * Fundamentals of machine learning | Multi-layer perceptron | Automatic differentiation | Training neural networks | Convolutional neural networks | Computer vision | Recurrent neural networks | Attention and transformers | Auto-encoders and variational auto-encoders | Generative adversarial networks | Uncertainty

  * [:tv: Lectures](https://www.youtube.com/playlist?list=PLLqXZ_E-UXlyGPyiWStnC_Y0iizSv5jsG) 

  * [:open_book: Slides/Lecture Notes](https://github.com/glouppe/info8010-deep-learning)

* [Université Paris Saclay - **Deep Learning**](https://github.com/m2dsupsdlclass/lectures-labs)

  * Embeddings and Recommender Systems | Convolutional Neural Networks for Image Classification | Deep Learning for Object Detection and Image Segmentation | Recurrent Neural Networks and NLP | Sequence to sequence, attention and memory | Expressivity, Optimization and Generalization | Imbalanced classification and metric learning | Unsupervised Deep Learning and Generative models 

  * [:open_book: Slides/Lecture Notes](https://m2dsupsdlclass.github.io/lectures-labs/)

  * [:memo: Notebooks](https://m2dsupsdlclass.github.io/lectures-labs/)

  

* [Stanford - **Analyses of Deep Learning (STATS385)**](https://stats385.github.io/lecture_slides)

* [Portland State University - **Deep Learning Theory and Practice (ECE510)**](http://web.cecs.pdx.edu/~willke/courses/EE510W20/)

* [University of Amsterdam - **Deep Learning Course**](https://uvadlc.github.io/)

* [Berkeley - **Designing, Visualizing and Understanding Deep Neural Networks (CS W182)**](https://cs182sp21.github.io/)

## Data Mining

* [Harvard - **Introduction to Data Science (CS109a)**](https://harvard-iacs.github.io/2021-CS109A/pages/materials.html)

* [Stanford - **Mining Massive Data Sets (CS246)**](http://web.stanford.edu/class/cs246/index.html#content)

* [Stanford - **Data Mning and Analysis (Stats 202)**](http://web.stanford.edu/class/stats202/intro.html)

## Machine Learning

* [CMU - 15-488: Machine Learning in a Nutshell (CMU)](https://web2.qatar.cmu.edu/~gdicaro/15488/)

* [Caltech - Learning From Data](https://work.caltech.edu/lectures.html)

* [Cornell - Advanced Machine Learning Systems (CS6787)](https://www.cs.cornell.edu/courses/cs6787/2019fa/)

* [CMU - Probabilistic Graphical Models (10-708)](https://www.cs.cmu.edu/~epxing/Class/10708-20/lectures.html)

* [Stanford - Machine Learning with Graphs (CS224W)](http://web.stanford.edu/class/cs224w/)

* [Stanford - CS 329S: Machine Learning Systems Design](https://stanford-cs329s.github.io/syllabus.html)

## Reinforcement Learning

* [Stanford - Reinforcement Learning (CS234)](https://web.stanford.edu/class/cs234/)

* [Berkeley - Deep Reinforcement Learning (CS 285)](http://rail.eecs.berkeley.edu/deeprlcourse/)

* [DeepMind - Introduction to Reinforcement Learning](https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver)

## Computer Vision

* [Stanford - Convolutional Neural Networks for Visual Recognition (CS231n)](http://cs231n.stanford.edu/schedule.html)

* [TUM - Advanced Deep Learning for Computer Vision](https://www.youtube.com/playlist?list=PLog3nOPCjKBkngkkF552-Hiwa5t_ZeDnh)

## Natural Language Processing

* :new::fire::fire::fire: [Stanford -**Transformers United V2**](https://web.stanford.edu/class/cs25/)

  * Transformers | Language and Human Alignment | Emergent Abilities and Scaling in LLMs | Strategic Games | Robotics and Imitation Learning | In-Context Learning & Faithful Reasoning | Neuroscience-Inspired Artificial Intelligence 

  * [:tv: Lectures](https://www.youtube.com/watch?v=XfpMkf4rD6E) 

  * [:open_book: Slides/Lecture Notes](https://web.stanford.edu/class/cs25/)

* :new::fire::fire::fire: [Stanford - **Large Language Models (CS324)**](https://stanford-cs324.github.io/winter2022/)

  * LLM Capabilities | Harms, Safety, and Ethics | LLM Data | Extracting Training Data | Objective Functions and Optimization | Scaling Laws | NLP architectures | Downstream Adaptation 

  * [:open_book: Slides/Lecture Notes](https://stanford-cs324.github.io/winter2022/)

  

* [EN 601.468/668 Machine Translation (JHU)](http://mt-class.org/jhu/syllabus.html)

* [CS224n: Natural Language Processing with Deep Learning (Stanford)](http://web.stanford.edu/class/cs224n/)

* [Info 159/259: Natural Language Processing (UC Berkeley)](https://people.ischool.berkeley.edu/~dbamman/nlp21.html#syllabus)

* [CS224u: Natural Language Understanding (Stanford)](https://web.stanford.edu/class/cs224u/)

* [CS11-747: Neural Networks for NLP (CMU)](http://www.phontron.com/class/nn4nlp2020/schedule.html)

## Math

* [Linear Algebra (MIT)](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)

* [18.065: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/)

* [CS109: Probability for Computer Scientists (Stanford)](http://web.stanford.edu/class/cs109/schedule.html)

* [Bayesian Data Analysis](https://avehtari.github.io/BDA_course_Aalto/)

* [Bayesian Methods Research Group](https://bayesgroup.ru/)

* [Mathematics of Deep Learning (Berkeley)](https://joanbruna.github.io/MathsDL-spring19/)

* [STAT 991: Topics in deep learning (UPenn)](https://github.com/dobriban/Topics-in-deep-learning)

## Computer Science

* :new::fire::fire::fire:  [Berkeley - **Data Structures (CS 61B)**](https://sp23.datastructur.es/)  

* [MIT 18.S191 Introduction to Computational Thinking (MIT)](https://computationalthinking.mit.edu/Spring21/)