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Please installOpen in Colab extension in Google Chrome in order to open a Github-hosted notebook in Google Colab with one-click.

The course is taught in collaboration with DeepMind. The majority of lectures will be taught by guest lecturers from DeepMind who are leading experts in the field of machine learning and will teach about topics in which they are specialised.

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This course, taught originally at UCL and recorded for online access, has two interleaved parts that converge towards the end of the course. One part is on machine learning with deep neural networks, the other part is about prediction and control using reinforcement learning. The two strands come together when we discuss deep reinforcement learning, where deep neural networks are trained as function approximators in a reinforcement learning setting.

• The deep learning stream of the course will cover a short introduction to neural networks and supervised learning with TensorFlow, followed by lectures on convolutional neural networks, recurrent neural networks, end-to-end and energy-based learning, optimization methods, unsupervised learning as well as attention and memory. Possible applications areas to be discussed include object recognition and natural language processing.
• The reinforcement learning stream will cover Markov decision processes, planning by dynamic programming, model-free prediction and control, value function approximation, policy gradient methods, integration of learning and planning, and the exploration/exploitation dilemma. Possible applications to be discussed include learning to play classic board games as well as video games.

[1] Richard S. Sutton, Andrew G. Barto."Reinforcement learning: an introduction". 1998.
[2] Csaba Szepesvári."Algorithms for reinforcement learning". 2010.
[3] Ian Goodfellow, Yoshua Bengio, Aaron Courville."Deep learning". 2016.