Sources
Here is a list of lectures and individual talks from around the world I want to watch or I have already watched and don't want to lose a track of it. Please send me an email, if you find any of these links not available anymore.
Economics
University courses video lectures:
- Financial Markets (2011) [Yale]
- Financial Theory [Yale]
- Základy finančního managementu (2018) [ČVUT]
- Finanční management (2018) [ČVUT]
- Game Theory [Yale]
- Computational Finance (2008) [Hong Kong University of Science and Technology]
- Introduction to Banking [University of Lepzig] (2020)
Computer Science
- Algorithms (CTU) - Marko
- Ryan ODonnel CMU - Complexity theory (graduate, under graduate), quantum complexity, great ideas in theoretical computer science
- Scott Aaronson on Computational Complexity Theory and Quantum Computers - book about quantum complexity
- Algoritmy a jejich implementace (Charles University)
- Datové struktury 2 (Charles University)
- Introduction to Electrical Engineering and Computer Science I [MIT]
- Computer Architecture [2015] (Carnegie Mellon University)
- Design and Analysis of Algorithms [MIT]
- Design and Analysis of Algorithms [Stanford University]
- Introduction to Algorithms (2011) [MIT]
- Algorithmic Game Theory (CS364A) [Stanford University] (2013)
- Advanced Mechanism Design (CS364B) [Stanford University] (2014)
- A Second Course in Algorithms (CS261) [Stanford University] (2016)
- Beyond Worst-Case Analysis (CS264) [Stanford University] (2014)
- Algorithms 1 [Stanford University]
- Advanced Algorithms [Harvard University] (2016)
- Introduction to Computer Systems [CMU] (2015), class website
- Great Theoretical Ideas of Computer Science [CMU] (2015)
- Advanced Topics in Programming Languages [CMU] (2013)
- Computer Graphics [Utrecht University](2013)
- Advanced Algorithms [MIT] (2013), class website
- Computation Geometry [University of Wisconsin-Madison] (2014)
- Graduate Artificial Intelligence [CMU] (2014)
- Modern C++ [Freiburg] (2018)
- Operation Research [SUNY Binghamton] (2018)
- Missing Semester in CS [MIT] (2020)
- Applications of Parallel Computers [UC Berkeley] (2020), web
- Computer Architecture [ETH Zurich] (2019) with GPU programming
- Algoritmy a dátové štruktúry (2019)[FMFI UK]
- Vulkan API on Linux
- Software Security [ASU] (2018)
- Principles of Programming Languages [ASU] (2016)
- Category Theory (2020)
- CS193p iPhone Application Development Spring [Stanford] (2020)
- Advanced Data Structures [MIT] (2012)
- Vybrané kapitoly z datových struktur [Charles University] (2020)
- CS221 AI: Principles and Techniques [Stanford University] (2020)
Individual talks:
- The Analysis of Algoriths by Donald Knuth (Stanford University)
- Edsger W. Dijkstra - Lecture: Reasoning About Programs - Solving 2 problems using programing - variants, invariants, correctness
- Trasovani programu na Linuxu
Mathematics
- Elements of Abstract and linear algebra
- Mathematical Tools for Physics
- Diskrétní matematika [2018] (ČVUT) - Habala
- Diskrétní matematika [Charles University] (2012)
- Mathematics - Making the invisible visible [Stanford] (2012)
- Introduction to probability [MIT] (2018)
- Differential Equations [MIT] (2010)
- Learn Differential Equations [MIT] (2015)
- Optimization (2012) [CMU] and class website
- Modelování a simulace dynamických systém (2014) [ČVUT]
- Matematický korespondeční seminář [Charles University]
- Probabilistic Systems Analysis and Applied Probability [MIT]
- Convex Optimization I (Stanford University) [2008]
- Convex Optimization I (Stanford University) [2014]
- Convex Optimization II (Stanford University) [2008]
- Discrete Stochastic Processes (MIT)
- Topics in Mathematics with Applications in Finance
- Mathematics for Computer Science [MIT]
- Probability and Statistics for Finance (Indian Institute of Technology)[2016]
- Základy matematické analýzy (ČVUT) [2017]
- Kombinatoricka optimalizace [CTU] (2020); lab tutorials, website
- Pravdepodovnost a Statistika (2020)[CTU FEL]
- Pravdepodovnost a Statistika (2019)[CTU FIT]
- Matematika pro informatiku (2020)[CTU FIT]
- Mathematics 4 - Real Analysis (2014)[DTU]
- Convex Optimization (2018)[CMU]
- Calculus 1 (2020) by Richard Hammack
- Networked Life (Upenn) [2020]
- No Regrets in Game Theory and Machine Learning (Upenn) [2013]
- Functional Analysis (UCCS)[2014]
mathematicalmonk's probability primar YouTube channelmathematicalmonk's information theory YouTube channel
University courses video lectures:
- Základy diskrétní matematiky (2016/2017) [Czech Technical University in Prague]
- Lineární algebra (2016/2017) [Czech Technical University in Prague]
- Introduction to Linear Dynamical Systems (2008) [Stanford University], class website
- Abstract Algebra
- Intermediate Statistics [CMU], class website
- Multivariable Calculus [MIT] (2010)
- Nonlinear Dynamics and Chaos [Cornell University] (2014)
- Math Background for Machine Learning [CMU] (2016)
- Math for Finance (2014) [University of California], class website
- Statistics 110 (Probability) [Harvard University](2013)
- Measure and Integration [NPTEL]
- Convex Optimization [CMU] (2018)
- Introduction to Bayesian Statistics [2017] (University of Auckland) - Recommended book sources are Doing Bayesian Data Analysis (blog of the author) and Information Theory, Inference and Learning (in this page I have videos and book link)
- Statistical Rethinking [Max Plank Institute for Evolutionary Anthropology] (2017), book website
- Diferencialni rovnice a numericka matematika [CVUT](2019) - Habala
Machine Learning
- Umělá inteligence [ČVUT](bakalářský kurz na kybernetice)
- Understanding Machine Learning (2016) [University of Waterloo]
- Machine learning for intelligent systems (2018) [Cornell University] - undergrad level
- Deep Learning for NLP (Oxford)
- Practical Machine learnig, ALgorithms from Stanford
- Machine learning 2017 - University of Buffalo
- Introduction to AI (Norvig)
- Artificial General Intelligence [MIT] (2018), class website
- Intro to Deep Learning [MIT] (2017)
- Artificial Intelligence [MIT] (2010)
- Artificial Intelligence [UC Berkley] (2013)
- Artificial Intelligence [UC Berkley] (2016)
- Learning Theory (2014) [Johns Hopkins University], lectures are related to the book Biological Learning and Control: How the Brain Builds Representations, Predicts Events, and Makes Decisions, website for the course is here.
- Machine Learning by Andrew Ng [Stanford University], official website for this course is here
- Graduate Artificial Intelligence [CMU] (2017), class website
- Machine Learning [CMU] (2006) for undergrads and masters, class website
- Introduction to Machine Learning [CMU] (2011) for PhD students, class website
- Practical Data Science [CMU] (2016), class website
- CS229 Machine Learning [Stanford] (2018)
- MultiModal Machine Learning [CMU] (2020), web
- Probabilistic Graph Models [CMU] (2020), Stanford notes
General machine learning
About EM algorithm:
- mathematicalmonk's EM (there are more than one video (watch!))
- Gaussian mixture and EM (best explanation yet) (WATCH!)
- EM tutorial on University of Toronto from course CSC412/2506 Winter 2019: Probabilistic Learning and Reasoning
- Information Theory, Pattern Recognition, and Neural Networks (2003) [University of Cambridge], website with a book
- Machine Learning [UBC] (2013)
- Machine Learning with Grapfs [Stanford University](2020), web
- Machine Learning Course by Lili Mou [Alberta Machine Intelligence Institute](Fall 2020)
- Machine Learning [EPFL] (2020) short videos
- Theory of machine learning (Upenn) [2020]
Books:
- Deep Learning (2018) [Charles University in Prague]
- Introduction do Deep Learning [CMU](2018), class website
- Topics in Deep Learning [CMU] (2017), class website
- Pattern Recognition and Application
- Machine Learning [UCB] (2012)
- Learning from Data [Caltech]
- Statistical Learning Theory and Applications [MIT] (2015)
- Statistical Learning Theory and Applications [MIT] (2017)
- Deep learning [Oxford University] (2015), website of the course
- Advanced Introduction to Machine Learning [CMU] (2015), class website
- Statistical Machine Learning [CMU] (2017), class website
- Statistical Machine Learning [CMU] (2016), class website
- Statistical Learning - book An Introduction to Statistical Learning with Application in R
- Machine Learning & Data Mining [University of California - Irvin] (2013)
mathematicalmonk's machine learning YouTube channel
- Machine Learning and Prediction in Economics and Finance
- Learning and Reasoning with Bayesian Networks [UCLA] (2018)
- Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)
Neural Networks
- VS265: Neural Computation [UC Berkeley](2020)
- Designing, Visualizing and Understanding Deep Neural Networks [UC Berkeley](2021); web
- Deep Learning [University of Waterloo] (2017)
- Data Visualization [University of Waterloo] (2017)
- Classification [University of Waterloo] (2017)
- CS 230 Deep Learning [Stanford] (2019) - cheat sheet (CS 221 AI, CS 229 ML, CS 230 DL), website
- CS 330 Deep Multi-task and Meta Learning [Stanford] (2019) -website
- Deep Unsupervised Learning (UC Berkeley) [2020], website
- Deep Learning STAT-157 (2019) [UC Berkeley] by Alex Smola and his book Dive into Deep Learning
- Deep Learning (2020) by Sargur N. Srihari
- Deep Learning Application to Natural Language Processing (2020) by Sargur N. Srihari
- Neural Nets for Natural Language Processing (2020) [CMU]
- CS224W Graph Neural Nets (2020) [Stanford University]
- Deep Learning (2020) [New York University] by Yann LeCun, website
- Neural Networks (2013) [University of Toronto] by Geoffrey Hinton
- Deep Learning [DeepMind] (2020)
- Deep Learning in Life Sciences [MIT] (2020)
Papers:
- Neural Ordinary Differential Equations (NIPS 2018 best paper); authout website, https://github.com/rtqichen/torchdiffeq, https://github.com/llSourcell/Neural_Differential_Equations/
- Advances in Variational Inference - nice review
- CS [L,W]182/282A Designing, Visualizing and Understanding Deep Neural Networks Spring 2020 (UC Berkeley)[2020], web
Individual talks:
- What is wrong with convolutional neural nets? by Geoffrey Hinton (2017)
- Reccurent neural networks [Stanford] this is one lecture from the whole course
- Introduction to Convolutional Neural Networks for Visual Recognition
- Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization) [Andrew Ng] (2017)
- Structuring Machine Learning Projects (Course 3 of the Deep Learning Specialization) [Andrew Ng] (2017)
Useful links:
Practical:
Reinforcement learning
Educational material:
University courses video lectures:
- Deep Reinforcement Learning and Control [CMU] (2018), class website
- Intelligent Systems (Intro to AI) [University of Alberta] (2016) , class website
- Reinforcement Learning for Artificial Intelligence [University of Alberta] (2016) , class website
- Dynamic Programming and Stochastic Control [MIT]
- Reinforcement Learning [University of Edinburgh] (2015/1026)
- Deep reinforcement learning (2017) [UC Berkley]
- Reinforcement Learning by David Silver (2015) [UCL/Google Deepmind]
- Reinforcement Learning [NPTEL]
- Advanced Deep Learning and Reinforcement Learning [UCL/Deepmind] (2018)
- Deep Reinforcement Learning [University of Waterloo] (2018), class website
- Reinforcement Learning and Optimal Control (2019) [MIT/Arizona State University] by Dimitri P. Bertsekas
- Deep Reinforcement Learning: CS 285 Fall 2020 [UC Berkeley], web
Individual talks:
- MuZero - ICAPS 2020
- MIT 6.S191 Lecture 6: Deep Reinforcement Learning (2017) [MIT]
- A Tutorial on Reinforcement Learning I by Emma Brunskill (2017) [CMU]
- A Tutorial on Reinforcement Learning II by Emma Brunskill (2017) [CMU]
- Introduction to Reinforcement Learning with Function Approximation by Richard Sutton (2016) [University of Alberta]
- Deep reinforcement learning by David Silver (ICML 2016) [UCL/Google Deepmind]
- Deep reinforcement learning by David Silver (ICLR 2015) [UCL/Google Deepmind]
- Introduction to reinforcement learning (2008) [University of Alberta]
- ConvNetJS Deep Q Learning Demo by Andrej Karpathy
- Guest Post (Part I): Demystifying Deep Reinforcement Learning
- Deep Reinforcement Learning by David Silver on DeepMind's blog
- Deep Reinforcement Learning: Pong from Pixels by Andrej Karpathy
- ReinforceJs by Andrej Karpathy
- Deep Learning Summer School, Montreal 2016
- Deep Reinforcement Learning Bootcamp 2017 (Berkley)
- NIPS 2017 - AlphaZero by David Silver
- TRPO (Trust Region Policy Optimization) : In depth Research Paper Review
- Reinforcement Learning Class: DDPG
- Reinforcement Learning Class: DDPG
- Deep Learning and Reinforcement Learning Summer School, Toronto 2018
- Deep Learning and Reinforcement Learning Summer School, Montreal 2017
- Deep Learning and Reinforcement Learning Summer School, Montreal 2016
- Deep RL Bootcamp Lecture 5: Natural Policy Gradients, TRPO, PPO
- Microsoft Research - Policy Gradient Methods: Tutorial and New Frontiers 2017
Orial Vinyals - AlphaStar NIPS 2019
Papers:
- Deterministic Policy Gradient Algorithms
- A Natural Policy Gradient
- Science: A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
- Deep Visual Foresight for Planning Robot Motion
- Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
- Attentive Neural Processes
Useful links:
- Arxiv Insights - PPO, TRPO, Alpha Go, Attention
- https://github.com/aikorea/awesome-rl - similar summary website as mine for RL
- Deep Reinforcement Learning and Control a course from The Robotics Institute Carnegie Mellon University
- Counterfactual regret minimization for Poker AI
Hiearchical Temporal Memory
Useful links:
Physics
Useful links:
- Teoretická mechanika - přednáška 2 - variační počet a Langraengovy rovnice a skripta
- Fyzikální olympiáda
- Fykosák YouTube kanál
- Kvantova mechanika 1. (book)
- Statistical Mechanics I: Statistical Mechanics of Particles [MIT]
- Quantum Field Theory [Leibniz U Hannover]
- Mathematical methods of Quantum Information Theory [Leibniz U Hannover]
- Advanced Quantum Field Theory [Leibniz U Hannover]
- Quantum Field Theory [University of Cambridge]
- Theoretical minimum by L. Susskind [Stanford University]
University lectures:
- Classical Mechanics [MIT] (2016)
- Classical Mechanics (2011) [Stanford Universtiy] from Leonard Susskind'sTheoretical Minimum
- Teoretická mechanika [ČVUT] (2016)
- Elektromagnetismus a optika [Univerzita Komenského v Bratislavě] (2016)
Control Theory and Robotics
Links:
Biomechanics and Motor Control [CMU]
University courses video lectures:
- Robot Mapping [Freiburg] (2013)
- Signals and Systems [MIT] (1987)
- Signals and Systems [MIT] (2011)
- Signals and Systems [Stanford] (1999), class website
- Electronic Feedback Systems [MIT] (1985)
- Introduction to Robotics [Stanford University] (2008)
- Robot Kinematics and Dynamics [CMU] (2014), class website
- Robot Mechanics and Control
- Modeling dynamical systems and control
- Brian Dougles's control theory videos
- Optimal control theory [ČVUT] (-2020) - website
- Control Bootcamp - a mini course about optimal control theory
- Advanced Robotics (2011) [UC Berkley] by Pieter Abbeel, class website
- Advanced Robotics (UC Berkeley) [2019], website
- Humanoid Robotics (2013) [IHMC]
- Systémy a řízení (dnes Automatické řízení) [ČVUT]
- Control of Mobile Robots
- Underactuated Robotics (2009) [MIT] by Russ Tedrake - EdX course (2015), MIT course video lectures (2015), Drake library
- Underactuated Robotics [MIT] (2018), class website, Russ Tedrake's Github
- Optimal Control [University of Florida] (2012), class website
- Dynamics and Control [University of Briston] (2015)
- Linearni systemy [CTU] (2020), web
- Dynamics and Control of Networks [CTU] (2020), web
Individual talks:
IOTA
Links:
Videos
Psychology
- Introduction to Cognitive Neuroscience (MIT)
- Human Behavioral Biology [2010] (Stanford)
- Personality and its transformations [2015] (University of Toronto)
- Maps of Meaning [2016] (University of Toronto)
- Introduction to Psychology [2014] (Yale University)
- The Human Brain [2018] (MIT)
- Introduction to Psychology (MIT) [2011]
- Brains, Minds and Machines Summer Course (MIT) [2015]
- Thinking & Reasoning (PSY370) - John Vervaeke [2018] (University of Toronto)
Programming
Video lectures:
- Haskell - Functional Programming Fundamentals, by Dr. Eric Meijer following Programming in Haskell. He has also MOOC on edXIntroduction to Functional Programming