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Artificial Intelligence
CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW
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CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2015
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6.034 Artificial Intelligence, MIT OCW
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CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University
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15-780 - Graduate Artificial Intelligence, Spring 14, CMU
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CSE 592 Applications of Artificial Intelligence - University of Washington
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CS322 - Introduction to Artificial Intelligence - UBC (YouTube)
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CS 4804: Introduction to Artificial Intelligence
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CS 5804: Introduction to Artificial Intelligence
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Artificial Intelligence - IIT Kharagpur
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Artificial Intelligence - IIT Madras
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Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur
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MOOC - Intro to Artificial Intelligence - Udacity
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MOOC - Artificial Intelligence for Robotics - Udacity
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Graduate Course in Artificial Intelligence - University of Washington
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Agent-Based Systems - University of Edinburgh
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Informatics 2D - Reasoning and Agents - University of Edinburgh
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Artificial Intelligence - Hochschule Ravensburg-Weingarten
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Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany
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Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
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Semantic Web Technologies by Dr. Harald Sack - HPI
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Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack - HPI
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T81-558: Applications of Deep Neural Networks by Jeff Heaton, Washington University in St. Louis
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Machine Learning
High Dimensional Analysis: Random Matrices and Machine Learning by Roland Speicher(Youtube)
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ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming
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EPFL COM 516 Markov Chains and Algorithmic Applications spring 2020, by Olivier Leveque
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CMU Advanced NLP 2021
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CMU Neural Nets for NLP 2021
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Natural Language Processing - Michael Collins - Columbia University
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CMU CS11-737 - Multilingual Natural Language Processing
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UMass CS685: Advanced Natural Language Processing (Spring 2022)
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Natural Language Processing (CMSC 470)
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CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
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CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)
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Machine Learning for Robotics and Computer Vision, WS 2013/2014 - TU München (YouTube)
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Informatics 1 - Cognitive Science 2015/16- University of Edinburgh
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Informatics 2A - Processing Formal and Natural Languages 2016-17 - University of Edinburgh
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Computational Cognitive Science 2015/16- University of Edinburgh
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NOC:Deep Learning For Visual Computing - IIT Kharagpur
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Deep Learning for Computer Vision - University of Michigan
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Extreme Classification
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02417 Time Series Analysis
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Applied Time Series Analysis
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Optimisation for Machine Learning: Theory and Implementation (Hindi) - IIT
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EE364a: Convex Optimization I - Stanford University
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10-725 Convex Optimization, Spring 2015 - CMU
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10-725 Convex Optimization: Fall 2016 - CMU
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10-725 Optimization Fall 2012 - CMU
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10-801 Advanced Optimization and Randomized Methods - CMU (YouTube)
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AM 207 - Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University
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Quantum Machine Learning | 2021 Qiskit Global Summer School
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CS 6955 - Clustering, Spring 2015, University of Utah
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Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information (YouTube)
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CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas
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CS224W Machine Learning with Graphs | Spring 2021 | Stanford University
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9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT
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Reinforcement Learning - UCL
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Regularization Methods for Machine Learning 2016 (YouTube)
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Statistical Inference in Big Data - University of Toronto
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Reinforcement Learning 2015/16- University of Edinburgh
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Reinforcement Learning - IIT Madras
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Statistical Rethinking Winter 2015 - Richard McElreath
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Music Information Retrieval - University of Victoria, 2014
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PURDUE Machine Learning Summer School 2011
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Foundations of Machine Learning - Blmmoberg Edu
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Introduction to reinforcement learning - UCL
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Advanced Deep Learning & Reinforcement Learning - UCL
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Web Information Retrieval (Proff. L. Becchetti - A. Vitaletti)
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Big Data Systems (WT 2019/20) - Prof. Dr. Tilmann Rabl - HPI
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Distributed Data Analytics (WT 2017/18) - Dr. Thorsten Papenbrock - HPI
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Introduction to Data-Centric AI - MIT
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Parallel Computing and Scientific Machine Learning
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Machine Learning System Design - System Design Fight Club
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UT Austin ECE 381V Bandits and Online Learning fall 2021, by Sanjay Shakkottai
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UCSD MATH 273B Information Geometry and its Applications winter 2022, by Melvin Leok
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Cornell ECE 5545 Machine Learning Hardware and Systems spring 2022, by Mohamed Abdelfattah
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Geometric Deep Learning - AMMI
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Math for Deep Learning — Andreas Geiger
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Applied Deep Learning 2022 - TU Wien
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Neural Networks: Zero to Hero - Andrej Karpathy
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CIS 522 - Deep Learning - U Penn
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UVA DEEP LEARNING COURSE
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Deep Learning (Fall 2020) - Georgia Tech
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CS7015 - Deep Learning - Prof. Mitesh M. Khapra - IIT Madras
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ETH Zürich | Deep Learning in Scientific Computing 2023
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CMU 10 707 Deep Learning fall 2017 by Ruslan Salakhutdinov
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UT Austin CS 394D Deep Learning fall 2021, by Philipp KrahenBühl
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Stanford CS25 - Transformers United 2023
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CS234: Reinforcement Learning - Winter 2019 - Stanford University
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Introduction to reinforcement learning - UCL
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Advanced Deep Learning & Reinforcement Learning - UCL
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Reinforcement Learning - IIT Madras
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CS885 Reinforcement Learning - Spring 2018 - University of Waterloo
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CS 285 - Deep Reinforcement Learning- UC Berkeley
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CS 294 112 - Reinforcement Learning
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NUS CS 6101 - Deep Reinforcement Learning
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ECE 8851: Reinforcement Learning
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CS294-112, Deep Reinforcement Learning Sp17 (YouTube)
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UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)
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Deep RL Bootcamp - Berkeley Aug 2017
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Reinforcement Learning - IIT Madras
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Reinforcement Learning Course at KTH (FDD3359 - 2022)
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Reinforcement Learning Course at ASU, Spring 2022
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CS 4789/5789: Introduction to Reinforcement Learning - Cornell
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S20/IE613 - Online (Machine) Learning/ Bandit Algorithms
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Reinforcement Learning - Fall 2021 chandar-lab
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CMU 10 703 Deep Reinforcement Learning & Control fall 2022, by Katerina Fragkiadaki
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Advanced Machine Learning, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI
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18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
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CS 330 - Deep Multi-Task and Meta Learning - Fall 2019 - Stanford University (Youtube)
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Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
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ES 661 (2023): Probabilistic Machine Learning - IIT Gandhinagar
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Information Retrieval in High Dimensional Data
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CS 224N -Natural Language Processing with Deep Learning - Stanford University (Lectures - Winter 2019) (Lectures - Winter 2021)
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CS 224N - Natural Language Processing, Stanford University (Lecture videos)
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Stanford XCS224U: Natural Language Understanding I Spring 2023
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CS388: Natural Language Processing - UT Austin
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CS 124 - From Languages to Information - Stanford University
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Neural Networks: Zero to Hero - Andrej Karpathy
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fast.ai Code-First Intro to Natural Language Processing (Github)
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MOOC - Natural Language Processing - Coursera, University of Michigan
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Natural Language Processing at UT Austin (Greg Durrett)
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CS224U: Natural Language Understanding - Spring 2019 - Stanford University
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Deep Learning for Natural Language Processing, 2017 - Oxford University
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Accelerated Natural Language Processing 2015/16- University of Edinburgh
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Natural Language Processing - IIT Bombay
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Data Warehousing and Data Mining Techniques - Technische Universität Braunschweig, Germany
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Data 8: The Foundations of Data Science - UC Berkeley (Summer 17)
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CSE519 - Data Science Fall 2016 - Skiena, SBU
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CS 109 Data Science, Harvard University (YouTube)
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6.0002 Introduction to Computational Thinking and Data Science - MIT OCW
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Data 100 - Summer 19- UC Berkeley
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Distributed Data Analytics (WT 2017/18) - HPI University of Potsdam
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Statistics 133 - Concepts in Computing with Data, Fall 2013 - UC Berkeley
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Data Profiling and Data Cleansing (WS 2014/15) - HPI University of Potsdam
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CS 229r - Algorithms for Big Data, Harvard University (Youtube)
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Algorithms for Big Data - IIT Madras
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Python Data Science with the TCLab (YouTube)
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MOOC - Probabilistic Graphical Models - Coursera
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CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah
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10-708 - Probabilistic Graphical Models, Carnegie Mellon University
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Probabilistic Graphical Models, Daphne Koller, Stanford University
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Probabilistic Models - UNIVERSITY OF HELSINKI
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Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh
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Probabilistic Graphical Models, Spring 2018 - Notre Dame
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Full Stack Deep Learning - Course 2022
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Full Stack Deep Learning - Course 2021
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NYU Deep Learning Spring 2020
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NYU Deep Learning Spring 2021
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6.S191: Introduction to Deep Learning - MIT
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Intro to Deep Learning and Generative Models Course - Prof Sebastian Raschka
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Deep Learning CMU
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CS231n Deep Learning for Computer Vision - Winter 2016 Andrej Karpathy - Stanford University
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Deep Learning: CS 182 Spring 2021
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10-414/714: Deep Learning Systems - CMU (Youtube)
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Part 1: Practical Deep Learning for Coders, v3 - fast.ai
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Part 2: Deep Learning from the Foundations - fast.ai
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Deep learning at Oxford 2015 - Nando de Freitas
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Self-Driving Cars — Andreas Geiger, 2021/22 (YouTube)
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6.S094: Deep Learning for Self-Driving Cars - MIT
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CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
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CS230: Deep Learning - Autumn 2018 - Stanford University
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STAT-157 Deep Learning 2019 - UC Berkeley
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Full Stack DL Bootcamp 2019 - UC Berkeley
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Deep Learning, Stanford University
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MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
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Deep Unsupervised Learning -- Berkeley Spring 2020
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Stat 946 Deep Learning - University of Waterloo
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Neural networks class - Université de Sherbrooke (YouTube)
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CS294-158 Deep Unsupervised Learning SP19
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DLCV - Deep Learning for Computer Vision - UPC Barcelona
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DLAI - Deep Learning for Artificial Intelligence @ UPC Barcelona
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Neural Networks and Applications - IIT Kharagpur
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UVA DEEP LEARNING COURSE
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Nvidia Machine Learning Class
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Deep Learning - Winter 2020-21 - Tübingen Machine Learning
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Machine Learning - Pedro Domingos- University of Washington
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Advanced Machine Learning - 2019 - ETH Zürich
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Machine Learning (COMP09012)
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Probabilistic Machine Learning 2020 - University of Tübingen
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Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen
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COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University
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Machine Learning for Engineers 2022 (YouTube)
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10-418 / 10-618 (Fall 2019) Machine Learning for Structured Data
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ORIE 4741/5741: Learning with Big Messy Data - Cornell
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Machine Learning in IoT
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Stanford CS229M: Machine Learning Theory - Fall 2021
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Intro to Machine Learning and Statistical Pattern Classification - Prof Sebastian Raschka
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CMU's Multimodal Machine Learning course (11-777), Fall 2020
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EE104: Introduction to Machine Learning - Stanford University
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CPSC 330: Applied Machine Learning (2020) - UBC
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Machine Learning 2013 - Nando de Freitas, UBC
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Machine Learning, 2014-2015, University of Oxford
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10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU (Spring 2015)
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10-715 Advanced Introduction to Machine Learning - CMU (YouTube)
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CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley
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100 Days of Machine Learning - CampusX (Hindi)
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CampusX Data Science Mentorship Program 2022-23 (Hindi)
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Statistical Machine Learning - S2023 - Benyamin Ghojogh
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MIT 6.5940 EfficientML.ai Lecture, Fall 2023
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TinyML - Tiny Machine Learning at UPenn
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Machine Learning Hardware and Systems (Cornell Tech, Spring 2022)
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ECE 4760 (Digital Systems Design Using Microcontrollers) at Cornell for the Fall, 2022
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EfficientML.ai Lecture, Fall 2023, MIT 6.5940
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CS189 Machine Learning 2022 - UCB
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ETH Zurich Statistical Learning Theory spring 2021, by Joachim M. Buhmann
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SFU CMPT 727 Statistical Machine Learning spring 2022, 2023, by Maxwell Libbrecht
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UC Berkeley CS 189 / 289A Introduction to Machine Learning fall 2023, by Jennifer Listgarten & Jitendra Malik
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UC Berkeley CS 189 / 289A Introduction to Machine Learning spring 2022, by Jonathan Shewchuk
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UC Berkeley CS 189 Introduction to Machine Learning (CDSS offering) spring 2022, by Marvin Zhang
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MIT 6.036 Introduction to Machine Learning spring 2019, by Leslie Kaelbling
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UCLA STAT C161 Introduction to Pattern Recognition and Machine Learning winter 2023, by Arash Amini
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UT Austin Machine Learning Algorithms & Statistical Learning by Adam Klivans & Qiang Liu
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MSU Machine Learning
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CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington (YouTube)
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MOOC - Text Mining and Analytics by ChengXiang Zhai
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Information Retrieval SS 2014, iTunes - HPI
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MOOC - Data Mining with Weka
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CS 290 DataMining Lectures
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Data Mining: Learning From Large Datasets - Fall 2017 - ETH Zurich
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Information Retrieval - Spring 2018 - ETH Zurich
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Introduction to Machine Learning for Coders
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MOOC - Statistical Learning, Stanford University
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Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
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CS 156 - Learning from Data, Caltech
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10-601 Machine Learning | CMU | Fall 2017
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10 - 301/601 - Introduction to Machine Learning - Spring 2020 - CMU
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10 - 301/601 - Introduction to Machine Learning - Fall 2023 - CMU
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6.036 - Machine Learning, Broderick - MIT Fall 2020
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Mediterranean Machine Learning summer school 2023
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Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)
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CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech
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Microsoft Research - Machine Learning Course
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CS 446 - Machine Learning, Fall 2016, UIUC
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undergraduate machine learning at UBC 2012, Nando de Freitas
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CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
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CPSC 340: Machine Learning and Data Mining (2018) - UBC
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CS4780/5780 Machine Learning, Fall 2013 - Cornell University
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CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo
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CS 5350/6350 - Machine Learning, Fall 2016, University of Utah
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ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
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CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
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STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
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CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
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STAT 441/841 Classification Winter 2017 , Waterloo
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10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
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Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
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Python and machine learning - Stanford Crowd Course Initiative
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Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
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Introductory Applied Machine Learning 2015/16- University of Edinburgh
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Pattern Recognition Class (2012)- Universität Heidelberg
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Introduction to Machine Learning and Pattern Recognition - CBCSL OSU
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Introduction to Machine Learning - IIT Kharagpur
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Introduction to Machine Learning - IIT Madras
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Pattern Recognition - IISC Bangalore
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Pattern Recognition and Application - IIT Kharagpur
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Pattern Recognition - IIT Madras
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Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
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Machine Learning - Professor Kogan (Spring 2016) - Rutgers
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Machine Learning Crash Course 2015
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COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
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10715 Advanced Introduction to Machine Learning
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Introduction to Machine Learning - Spring 2018 - ETH Zurich
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