Lectures based on a course of A. S. Bandeira at ETH Zurich, and on a book co-authored with Amit Singer and Thomas Strohmer. Recordings made due to the COVID-...

CS 181 Spring 2024 Website. Contribute to harvard-ml-courses/cs181-web development by creating an account on GitHub.

Lecture videos and materials from the Deep Generative Models course at Cornell and Cornell Tech, taught in Spring 2023. Instructor: https://www.cs.cornell.ed...

First half of a course on Mathematics of Machine Learning. Covering Unsupervised Learning and Parsimony. Second half, taught by Zhivotovskiy, will be availab...

Presented by Dr. Anthony Bosman.

The class starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many...

Capture, manage, and search all your video content.

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai View the course website: https://deepgenerativemodels.gi...

When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking ...

Lectures, slides, and information for MIT's 6.036 course: Introduction to Machine Learning (Fall 2020)

Public repository of class notes: https://github.com/smileschen/2021-sp-stats21

CS 189/289A (Introduction to Machine Learning) covers: Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression...

CME 213 Spring 2021. Contribute to EricDarve/cme213-spring-2021 development by creating an account on GitHub.

https://inst.eecs.berkeley.edu/~cs188/sp23/

Lectures of the course "Mathematics for Machine Learning" by Ulrike von Luxburg, University of Tübingen, Winter Term 2020/21. Updated slides (with typos corr...

people.csail.mit.edu/moitra/854.html

Course Website & Materials: https://kuleshov-group.github.io/aml-website/ Lecture Notes: https://kuleshov-group.github.io/aml-book/intro.html Course Material...

Math 300 at the University of Washington, Winter 2021

Detailed lecture notes (book draft) https://www.weizmann.ac.il/complex/falkovich/sites/complex.falkovich/files/uploads/PNI22.pdf

Learning theory is a rich field at the intersection of statistics, probability, computer science, and optimization. Over the last decades the statistical lea...

This is a teaching partnership between the Africa Center of Excellence in Data Science at the University of Rwanda and the Institute of Applied Computational Science at Harvard University.

MIT Open Learning Library https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/course/

This course is designed to teach deep learning for computer vision. It aims to teach material from introductory deep learning all the way to some state-of-th...

View the course website: https://stanforddatacompressionclass.github.io/Fall23/ The amount of data being generated, stored and communicated by humanity is gr...

MML Tutorial CVPR 2022 NAACL 2022 schedule resources Schedule ** Exact topics and schedule subject to change, based on interests and time. ** Part Topics Rea...

Channel for Paderborn University’s initiative on ‘Data Science for Dynamical Systems’ that addresses open education and science contributions in control engineering and machine learning. Hosts:...

This playlist accompanies the class "Linear Programming and Network Flows", Math 510, at Colorado State University. The notes for these videos are available ...

EPFL Course - Optimization for Machine Learning - CS-439 - epfml/OptML_course

Machine learning videos from courses taught at UBC by Varada Kolhatkar

Lectures from ECE524 Foundations of Reinforcement Learning at Princeton University, Spring 2024. This course is a graduate level course, focusing on theoreti...

Professor Hima Lakkaraju's day-long workshop at Stanford covered modern techniques for interpretable machine learning. About the speaker: Himabindu (Hima) La...

This is a playlist of all recitations for the 11785 Introduction to Deep Learning course at Carnegie Mellon University for Spring 2024.

This playlist contains the video lectures of the UW-Madison course "Linear Optimization - ISyE/Math/CS/Stat 525" taught by Professor Alberto Del Pia in Fall ...

Playlist of the causal inference course lectures, where each video is a full lecture, rather than each video as a chunk of a lecture like in the other playlist.

Beta-Bernoulli Update -- Course Information Lectures: MW 11:25am-12:40pm, Mann 107 Instructor: Sid Banerjee, 229 Rhodes Hall, email Teaching Assistant: Spencer Peters Recitation and office hours...

Das Portal für Vorlesungsaufzeichnungen der Universität Erlangen-Nürnberg und Aufzeichnungen anderen Veranstaltungen der FAU.

Prerequisites: background in analysis, e.g., familiarity with the notion of uniform convergence, as covered in Course 7: Principles of Mathematical Analysis

1. Methods, history, definitions, Turing Test 2. Turing Test, Chinese Room, Generalization, PCA 3. PCA, AI and Data 4. LDA, t-SNE 5. Computer Vision, Feature...