Higher School of Economics

Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. –- with math & batteries included - using deep neural networks for RL tasks –- also known as "the hype train"

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.

Simon Kordonsky divides the social structure of contemporary Russia into distinct estates or social groups and describes each organization's unique resource-based political and economic nature.

This book analyses the egalitarian foundations of equality law from a classical liberal perspective by asking two central questions: does justice ideally demand equality? Are differences in abilities among people in some sense unfair? The book examines these questions in the context of racial diversity.