Course

CS231n: Convolutional Neural Networks for Visual Recognition

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks…

Statistical Learning Theory

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function…

CS 20SI: Tensorflow for Deep Learning Research

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of…

6.S094: Deep Learning for Self-Driving Cars

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in…

CS 294: Deep Reinforcement Learning (UC Berkeley)

Instructors: Sergey Levine, John Schulman, Chelsea Finn

Lectures: Mondays and Wednesdays, 9:00am-10:30am in 306 Soda Hall.

Machine Learning

Stanford University

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding…

Build Intelligent Applications

University of Washington

This Specialization provides a case-based introduction to the exciting, high-demand field of machine learning. You’ll learn to analyze large and complex datasets, build applications that can make predictions from data, and…