Advanced Computer Science (MPhil)
Lecturer: Rika Antonova 

This course introduces state-of-the-art reinforcement learning (RL) methods, covering theory and practice from foundational concepts to deep RL and applications. Topics include Bellman optimality, DQN and Q-Learning, policy gradient and actor-critic methods, exploration and uncertainty, model-based RL, RLHF for foundation models, and RL for scientific discovery.

Computer Science (Undergraduate)
Lecturers: Ferenc Huszar, Rika Antonova, Nic Lane

This course explores modern neural networks from theory to applications. It covers the basics of optimization (e.g., SGD, Adam), convergence, generalization, automatic differentiation, and mainstream architectures (e.g., CNNs, Transformers). Advanced topics include self-supervised learning, transfer learning, and graph neural networks.

Engineering (Tripos Part IIB)
Lecturers: Fumiya Iida, Fulvio Forni, Rika Antonova 

This course builds on introductory robotics and covers advanced topics including underactuated robotics, robot learning, soft robotics, human–robot interaction, and multi-agent systems. Students gain theoretical background and practical experience with collaborative research projects.