jvilltolentino/awesome-courses

Awesome AI/ML Courses

🎓 Awesome AI/ML Courses

Deep Learning

• DeepMind -The Deep Learning Lecture Series

  • 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
  • 📺 Lectures
  • 📖 Slides/Lecture Notes

• NYI Center for Data Science - Yann LeCun’s Deep Learning Course

  • Parameters sharing Energy based models | Associative Memories | Graphs | Control | Optimization
  • 📺 Lectures
  • 📖 Slides/Lecture Notes
  • 📝 Notebooks

• Université de Liège - Deep Learning (INFO8010)

  • 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
  • 📺 Lectures
  • 📖 Slides/Lecture Notes

• Université Paris Saclay - Deep Learning

  • 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
  • 📖 Slides/Lecture Notes
  • 📝 Notebooks

• Stanford - Analyses of Deep Learning (STATS385)

• Portland State University - Deep Learning Theory and Practice (ECE510)

• University of Amsterdam - Deep Learning Course

• Berkeley - Designing, Visualizing and Understanding Deep Neural Networks (CS W182)

Data Mining

• Harvard - Introduction to Data Science (CS109a)

• Stanford - Mining Massive Data Sets (CS246)

• Stanford - Data Mning and Analysis (Stats 202)

Machine Learning

• CMU - 15-488: Machine Learning in a Nutshell (CMU)

• Caltech - Learning From Data

• Cornell - Advanced Machine Learning Systems (CS6787)

• CMU - Probabilistic Graphical Models (10-708)

• Stanford - Machine Learning with Graphs (CS224W)

• Stanford - CS 329S: Machine Learning Systems Design

Reinforcement Learning

• Stanford - Reinforcement Learning (CS234)

• Berkeley - Deep Reinforcement Learning (CS 285)

• DeepMind - Introduction to Reinforcement Learning

Computer Vision

• Stanford - Convolutional Neural Networks for Visual Recognition (CS231n)

• TUM - Advanced Deep Learning for Computer Vision

Natural Language Processing

• 🆕🔥🔥🔥 Stanford -Transformers United V2

  • 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
  • 📺 Lectures
  • 📖 Slides/Lecture Notes

• 🆕🔥🔥🔥 Stanford - Large Language Models (CS324)

  • LLM Capabilities | Harms, Safety, and Ethics | LLM Data | Extracting Training Data | Objective Functions and Optimization | Scaling Laws | NLP architectures | Downstream Adaptation
  • 📖 Slides/Lecture Notes

• EN 601.468/668 Machine Translation (JHU)

• CS224n: Natural Language Processing with Deep Learning (Stanford)

• Info 159/259: Natural Language Processing (UC Berkeley)

• CS224u: Natural Language Understanding (Stanford)

• CS11-747: Neural Networks for NLP (CMU)

Math

• Linear Algebra (MIT)

• 18.065: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)

• CS109: Probability for Computer Scientists (Stanford)

• Bayesian Data Analysis

• Bayesian Methods Research Group

• Mathematics of Deep Learning (Berkeley)

• STAT 991: Topics in deep learning (UPenn)

Computer Science

• 🆕🔥🔥🔥 Berkeley - Data Structures (CS 61B)
• MIT 18.S191 Introduction to Computational Thinking (MIT)