study resources
this page is being updated actively with new resources for machine learning ...
A curated collection of free, high-quality machine learning resources: books and courses I’ve found valuable, whether you’re just starting out or already experienced. Nothing is hosted here; every link goes to the original source. The list began as my Machine-Learning-Resources GitHub repository. Have a suggestion? Send it through this Google Form.
Books on machine learning and related math, statistics and computer science, newest first.
No Cover Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems
No Cover Machine Learning in Production: From Models to Products
No Cover Foundations of Computer Vision
No Cover Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications
No Cover Deep Learning: Foundations and Concepts
No Cover Bayesian Optimization Book
No Cover Mathematical Analysis of Machine Learning Algorithms
No Cover An introduction to Optimization on Smooth Manifolds
No Cover Introduction to Probability for Computing
No Cover Linear Algebra Done Right
Supplementary materials:
No Cover Machine Learning for Data Streams with Practical Examples in MOA
No Cover Automata Theory: An Algorithmic Approach
No Cover Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks
Supplementary materials:
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Information Theory: From Coding to Learning
No Cover Telling Stories with Data: With Applications in R
No Cover Bayesian models of perception and action: An introduction
No Cover The Little Book of Deep Learning
No Cover Understanding Deep Learning
No Cover Probabilistic Machine Learning: Advanced Topics
No Cover Distributional Reinforcement Learning
No Cover Fairness and Machine Learning: Limitations and Opportunities
No Cover Learning Theory from First Principles
No Cover Machine learning with neural networks
Supplementary materials:
No Cover Patterns, Predictions, and Actions: A story about machine learning
No Cover Optimization for Modern Data Analysis
No Cover Random Matrix Methods for Machine Learning
No Cover
Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning
No Cover Graph Neural Networks: Foundations, Frontiers, and Applicationsg
No Cover
Reinforcement Learning: Theory and Algorithms
No Cover Introduction to Online Convex Optimization
No Cover Machine Learning - A First Course for Engineers and Scientists
No Cover Large-Scale Convex Optimization
No Cover Algorithms for Decision Making
No Cover The Principles of Deep Learning Theory
No Cover Hands-On Data Visualization
No Cover Probabilistic Numerics
No Cover Model-based Machine Learning
No Cover Probabilistic Machine Learning: An Introduction
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Speech and Language Processing
No Cover High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
No Cover Explanatory Model Analysis
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Machine Learning Interviews
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Learning Statistical Models Through Simulation in R
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Deep Learning Interviews
No Cover Data Science at the Command Line
No Cover Deep Learning on Graphs
No Cover Bayes Rules!
No Cover Physics-based Deep Learning Book
No Cover Introduction to Probability for Data Science
No Cover Theory of Computation
No Cover
Machine Learning and Big Data
No Cover Think Bayes
No Cover Computer Vision: Algorithms and Applications
No Cover An Introduction to Statistical Learning
No Cover Dive into Deep Learning
No Cover Bandit Algorithms
Supplementary materials:
No Cover Machine Learning from Scratch
No Cover Mathematics for Machine Learning
No Cover
Bayesian Reasoning and Machine Learning
No Cover Linear Algebra
No Cover Automated Machine Learning: Methods, Systems, Challenges
No Cover Introduction to Probability
No Cover Algorithmic Aspects of Machine Learning
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Adversarial Robustness - Theory and Practice
No Cover Foundations of Machine Learning
No Cover Reinforcement Learning: An Introduction
No Cover Elements of Statistical Learning
No Cover
A Course in Machine Learning
No Cover Computer Age Statistical Inference: Algorithms, Evidence and Data Science
No Cover Python Data Science Handbook
No Cover Deep Learning
No Cover Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
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Neural Networks and Deep Learning
No Cover Statistical inference for data science
No Cover Statistical Learning with Sparsity: The Lasso and Generalizations
No Cover Neural Network Design
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Algorithmic Aspects of Machine Learning
No Cover Understanding Machine Learning: From Theory to Algorithms
No Cover Think Stats
No Cover
The Matrix Cookbook
No Cover Algorithms for Reinforcement Learning
No Cover Convex Optimization
Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
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A Brief Introduction to Neural Networks
No Cover Gaussian Processes for Machine Learning
No Cover Pattern Recognition and Machine Learning
No Cover Information Theory, Inference, and Learning Algorithms
No Cover Machine Learning
No books found.
Free courses with lecture videos or notes, newest first. More are moving over from the GitHub repository.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan
Stat 156 / 256: Causal Inference
Advanced Latent Variable Modeling
Neural Networks: Zero to Hero
Multi Modal Machine Learning
Description
This revised version of CMU Multimodal Machine Learning course presents the fundamental mathematical concepts in machine learning and deep learning relevant to the six main challenges in multimodal research: (1) representation, (2) alignment, (3) reasoning, (4) generation, (5) transference and (6) quantification. This revised course is based on the new taxonomy introduced in this survey paper: https://arxiv.org/abs/2209.03430
CS 224W: Machine Learning with Graphs
CS25: Transformers United
CS W182 / 282A: Designing, Visualizing, and Understanding Deep Neural Networks
Introduction to Causal Inference
6.S897: Machine Learning For Healthcare
Random Matrices:Theory and Practice
No courses found.