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Artificial Intelligence

CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW

CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2015

6.034 Artificial Intelligence, MIT OCW

CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University

15-780 - Graduate Artificial Intelligence, Spring 14, CMU

CSE 592 Applications of Artificial Intelligence - University of Washington

CS322 - Introduction to Artificial Intelligence - UBC (YouTube)

CS 4804: Introduction to Artificial Intelligence

CS 5804: Introduction to Artificial Intelligence

Artificial Intelligence - IIT Kharagpur

Artificial Intelligence - IIT Madras

Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur

MOOC - Intro to Artificial Intelligence - Udacity

MOOC - Artificial Intelligence for Robotics - Udacity

Graduate Course in Artificial Intelligence - University of Washington

Agent-Based Systems - University of Edinburgh

Informatics 2D - Reasoning and Agents - University of Edinburgh

Artificial Intelligence - Hochschule Ravensburg-Weingarten

Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany

Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras

Semantic Web Technologies by Dr. Harald Sack - HPI

Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack - HPI

T81-558: Applications of Deep Neural Networks by Jeff Heaton, Washington University in St. Louis

Machine Learning

High Dimensional Analysis: Random Matrices and Machine Learning by Roland Speicher(Youtube)

ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming

EPFL COM 516 Markov Chains and Algorithmic Applications spring 2020, by Olivier Leveque

CMU Advanced NLP 2021

CMU Neural Nets for NLP 2021

Natural Language Processing - Michael Collins - Columbia University

CMU CS11-737 - Multilingual Natural Language Processing

UMass CS685: Advanced Natural Language Processing (Spring 2022)

Natural Language Processing (CMSC 470)

CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University

CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)

Machine Learning for Robotics and Computer Vision, WS 2013/2014 - TU München (YouTube)

Informatics 1 - Cognitive Science 2015/16- University of Edinburgh

Informatics 2A - Processing Formal and Natural Languages 2016-17 - University of Edinburgh

Computational Cognitive Science 2015/16- University of Edinburgh

NOC:Deep Learning For Visual Computing - IIT Kharagpur

Deep Learning for Computer Vision - University of Michigan

Extreme Classification

02417 Time Series Analysis

Applied Time Series Analysis

Optimisation for Machine Learning: Theory and Implementation (Hindi) - IIT

EE364a: Convex Optimization I - Stanford University

10-725 Convex Optimization, Spring 2015 - CMU

10-725 Convex Optimization: Fall 2016 - CMU

10-725 Optimization Fall 2012 - CMU

10-801 Advanced Optimization and Randomized Methods - CMU (YouTube)

AM 207 - Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University

Quantum Machine Learning | 2021 Qiskit Global Summer School

CS 6955 - Clustering, Spring 2015, University of Utah

Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information (YouTube)

CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas

CS224W Machine Learning with Graphs | Spring 2021 | Stanford University

9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT

Reinforcement Learning - UCL

Regularization Methods for Machine Learning 2016 (YouTube)

Statistical Inference in Big Data - University of Toronto

Reinforcement Learning 2015/16- University of Edinburgh

Reinforcement Learning - IIT Madras

Statistical Rethinking Winter 2015 - Richard McElreath

Music Information Retrieval - University of Victoria, 2014

PURDUE Machine Learning Summer School 2011

Foundations of Machine Learning - Blmmoberg Edu

Introduction to reinforcement learning - UCL

Advanced Deep Learning & Reinforcement Learning - UCL

Web Information Retrieval (Proff. L. Becchetti - A. Vitaletti)

Big Data Systems (WT 2019/20) - Prof. Dr. Tilmann Rabl - HPI

Distributed Data Analytics (WT 2017/18) - Dr. Thorsten Papenbrock - HPI

Introduction to Data-Centric AI - MIT

Parallel Computing and Scientific Machine Learning

Machine Learning System Design - System Design Fight Club

UT Austin ECE 381V Bandits and Online Learning fall 2021, by Sanjay Shakkottai

UCSD MATH 273B Information Geometry and its Applications winter 2022, by Melvin Leok

Cornell ECE 5545 Machine Learning Hardware and Systems spring 2022, by Mohamed Abdelfattah

Geometric Deep Learning - AMMI

Math for Deep Learning — Andreas Geiger

Applied Deep Learning 2022 - TU Wien

Neural Networks: Zero to Hero - Andrej Karpathy

CIS 522 - Deep Learning - U Penn

UVA DEEP LEARNING COURSE

Deep Learning (Fall 2020) - Georgia Tech

CS7015 - Deep Learning - Prof. Mitesh M. Khapra - IIT Madras

ETH Zürich | Deep Learning in Scientific Computing 2023

CMU 10 707 Deep Learning fall 2017 by Ruslan Salakhutdinov

UT Austin CS 394D Deep Learning fall 2021, by Philipp KrahenBühl

Stanford CS25 - Transformers United 2023

CS234: Reinforcement Learning - Winter 2019 - Stanford University

Introduction to reinforcement learning - UCL

Advanced Deep Learning & Reinforcement Learning - UCL

Reinforcement Learning - IIT Madras

CS885 Reinforcement Learning - Spring 2018 - University of Waterloo

CS 285 - Deep Reinforcement Learning- UC Berkeley

CS 294 112 - Reinforcement Learning

NUS CS 6101 - Deep Reinforcement Learning

ECE 8851: Reinforcement Learning

CS294-112, Deep Reinforcement Learning Sp17 (YouTube)

UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)

Deep RL Bootcamp - Berkeley Aug 2017

Reinforcement Learning - IIT Madras

Reinforcement Learning Course at KTH (FDD3359 - 2022)

Reinforcement Learning Course at ASU, Spring 2022

CS 4789/5789: Introduction to Reinforcement Learning - Cornell

S20/IE613 - Online (Machine) Learning/ Bandit Algorithms

Reinforcement Learning - Fall 2021 chandar-lab

CMU 10 703 Deep Reinforcement Learning & Control fall 2022, by Katerina Fragkiadaki

Advanced Machine Learning, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI

18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT

CS 330 - Deep Multi-Task and Meta Learning - Fall 2019 - Stanford University (Youtube)

Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022

ES 661 (2023): Probabilistic Machine Learning - IIT Gandhinagar

Information Retrieval in High Dimensional Data

CS 224N -Natural Language Processing with Deep Learning - Stanford University (Lectures - Winter 2019) (Lectures - Winter 2021)

CS 224N - Natural Language Processing, Stanford University (Lecture videos)

Stanford XCS224U: Natural Language Understanding I Spring 2023

CS388: Natural Language Processing - UT Austin

CS 124 - From Languages to Information - Stanford University

Neural Networks: Zero to Hero - Andrej Karpathy

fast.ai Code-First Intro to Natural Language Processing (Github)

MOOC - Natural Language Processing - Coursera, University of Michigan

Natural Language Processing at UT Austin (Greg Durrett)

CS224U: Natural Language Understanding - Spring 2019 - Stanford University

Deep Learning for Natural Language Processing, 2017 - Oxford University

Accelerated Natural Language Processing 2015/16- University of Edinburgh

Natural Language Processing - IIT Bombay

Data Warehousing and Data Mining Techniques - Technische Universität Braunschweig, Germany

Data 8: The Foundations of Data Science - UC Berkeley (Summer 17)

CSE519 - Data Science Fall 2016 - Skiena, SBU

CS 109 Data Science, Harvard University (YouTube)

6.0002 Introduction to Computational Thinking and Data Science - MIT OCW

Data 100 - Summer 19- UC Berkeley

Distributed Data Analytics (WT 2017/18) - HPI University of Potsdam

Statistics 133 - Concepts in Computing with Data, Fall 2013 - UC Berkeley

Data Profiling and Data Cleansing (WS 2014/15) - HPI University of Potsdam

CS 229r - Algorithms for Big Data, Harvard University (Youtube)

Algorithms for Big Data - IIT Madras

Python Data Science with the TCLab (YouTube)

MOOC - Probabilistic Graphical Models - Coursera

CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah

10-708 - Probabilistic Graphical Models, Carnegie Mellon University

Probabilistic Graphical Models, Daphne Koller, Stanford University

Probabilistic Models - UNIVERSITY OF HELSINKI

Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh

Probabilistic Graphical Models, Spring 2018 - Notre Dame

Full Stack Deep Learning - Course 2022

Full Stack Deep Learning - Course 2021

NYU Deep Learning Spring 2020

NYU Deep Learning Spring 2021

6.S191: Introduction to Deep Learning - MIT

Intro to Deep Learning and Generative Models Course - Prof Sebastian Raschka

Deep Learning CMU

CS231n Deep Learning for Computer Vision - Winter 2016 Andrej Karpathy - Stanford University

Deep Learning: CS 182 Spring 2021

10-414/714: Deep Learning Systems - CMU (Youtube)

Part 1: Practical Deep Learning for Coders, v3 - fast.ai

Part 2: Deep Learning from the Foundations - fast.ai

Deep learning at Oxford 2015 - Nando de Freitas

Self-Driving Cars — Andreas Geiger, 2021/22 (YouTube)

6.S094: Deep Learning for Self-Driving Cars - MIT

CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)

CS230: Deep Learning - Autumn 2018 - Stanford University

STAT-157 Deep Learning 2019 - UC Berkeley

Full Stack DL Bootcamp 2019 - UC Berkeley

Deep Learning, Stanford University

MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera

Deep Unsupervised Learning -- Berkeley Spring 2020

Stat 946 Deep Learning - University of Waterloo

Neural networks class - Université de Sherbrooke (YouTube)

CS294-158 Deep Unsupervised Learning SP19

DLCV - Deep Learning for Computer Vision - UPC Barcelona

DLAI - Deep Learning for Artificial Intelligence @ UPC Barcelona

Neural Networks and Applications - IIT Kharagpur

UVA DEEP LEARNING COURSE

Nvidia Machine Learning Class

Deep Learning - Winter 2020-21 - Tübingen Machine Learning

Machine Learning - Pedro Domingos- University of Washington

Advanced Machine Learning - 2019 - ETH Zürich

Machine Learning (COMP09012)

Probabilistic Machine Learning 2020 - University of Tübingen

Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen

COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University

Machine Learning for Engineers 2022 (YouTube)

10-418 / 10-618 (Fall 2019) Machine Learning for Structured Data

ORIE 4741/5741: Learning with Big Messy Data - Cornell

Machine Learning in IoT

Stanford CS229M: Machine Learning Theory - Fall 2021

Intro to Machine Learning and Statistical Pattern Classification - Prof Sebastian Raschka

CMU's Multimodal Machine Learning course (11-777), Fall 2020

EE104: Introduction to Machine Learning - Stanford University

CPSC 330: Applied Machine Learning (2020) - UBC

Machine Learning 2013 - Nando de Freitas, UBC

Machine Learning, 2014-2015, University of Oxford

10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU (Spring 2015)

10-715 Advanced Introduction to Machine Learning - CMU (YouTube)

CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley

100 Days of Machine Learning - CampusX (Hindi)

CampusX Data Science Mentorship Program 2022-23 (Hindi)

Statistical Machine Learning - S2023 - Benyamin Ghojogh

MIT 6.5940 EfficientML.ai Lecture, Fall 2023

TinyML - Tiny Machine Learning at UPenn

Machine Learning Hardware and Systems (Cornell Tech, Spring 2022)

ECE 4760 (Digital Systems Design Using Microcontrollers) at Cornell for the Fall, 2022

EfficientML.ai Lecture, Fall 2023, MIT 6.5940

CS189 Machine Learning 2022 - UCB

ETH Zurich Statistical Learning Theory spring 2021, by Joachim M. Buhmann

SFU CMPT 727 Statistical Machine Learning spring 2022, 2023, by Maxwell Libbrecht

UC Berkeley CS 189 / 289A Introduction to Machine Learning fall 2023, by Jennifer Listgarten & Jitendra Malik

UC Berkeley CS 189 / 289A Introduction to Machine Learning spring 2022, by Jonathan Shewchuk

UC Berkeley CS 189 Introduction to Machine Learning (CDSS offering) spring 2022, by Marvin Zhang

MIT 6.036 Introduction to Machine Learning spring 2019, by Leslie Kaelbling

UCLA STAT C161 Introduction to Pattern Recognition and Machine Learning winter 2023, by Arash Amini

UT Austin Machine Learning Algorithms & Statistical Learning by Adam Klivans & Qiang Liu

MSU Machine Learning

CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington (YouTube)

MOOC - Text Mining and Analytics by ChengXiang Zhai

Information Retrieval SS 2014, iTunes - HPI

MOOC - Data Mining with Weka

CS 290 DataMining Lectures

Data Mining: Learning From Large Datasets - Fall 2017 - ETH Zurich

Information Retrieval - Spring 2018 - ETH Zurich

Introduction to Machine Learning for Coders

MOOC - Statistical Learning, Stanford University

Foundations of Machine Learning Boot Camp, Berkeley Simons Institute

CS 156 - Learning from Data, Caltech

10-601 Machine Learning | CMU | Fall 2017

10 - 301/601 - Introduction to Machine Learning - Spring 2020 - CMU

10 - 301/601 - Introduction to Machine Learning - Fall 2023 - CMU

6.036 - Machine Learning, Broderick - MIT Fall 2020

Mediterranean Machine Learning summer school 2023

Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)

CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech

Microsoft Research - Machine Learning Course

CS 446 - Machine Learning, Fall 2016, UIUC

undergraduate machine learning at UBC 2012, Nando de Freitas

CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley

CPSC 340: Machine Learning and Data Mining (2018) - UBC

CS4780/5780 Machine Learning, Fall 2013 - Cornell University

CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo

CS 5350/6350 - Machine Learning, Fall 2016, University of Utah

ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech

CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech

STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto

CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo

STAT 441/841 Classification Winter 2017 , Waterloo

10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU

Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge

Python and machine learning - Stanford Crowd Course Initiative

Machine Learning and Pattern Recognition 2015/16- University of Edinburgh

Introductory Applied Machine Learning 2015/16- University of Edinburgh

Pattern Recognition Class (2012)- Universität Heidelberg

Introduction to Machine Learning and Pattern Recognition - CBCSL OSU

Introduction to Machine Learning - IIT Kharagpur

Introduction to Machine Learning - IIT Madras

Pattern Recognition - IISC Bangalore

Pattern Recognition and Application - IIT Kharagpur

Pattern Recognition - IIT Madras

Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen

Machine Learning - Professor Kogan (Spring 2016) - Rutgers

Machine Learning Crash Course 2015

COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16

10715 Advanced Introduction to Machine Learning

Introduction to Machine Learning - Spring 2018 - ETH Zurich

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