This course presents an application-focused and hands-on approach to learning neural networks and reinforcement learning. It can be viewed as first introduction to deep learning methods, presenting a wide range of connectionist models which represent the current state-of-the-art. It explores the most popular algorithms and architectures in a simple and intuitive style.
Topics and features: the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network; a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.
The course can be of interest for graduate and advanced undergraduate students of statistics, computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.