Imperial College London

Dr A. Aldo Faisal

Faculty of EngineeringDepartment of Bioengineering

Reader in Neurotechnology
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Reinforcement Learning - CO424

Aims

The course provides both basic and advanced knowledge in reinforcement learning across three core skills: theory, implementation, and evaluation. Students will learn the fundamentals of both tabular reinforcement learning and deep reinforcement learning, and will gain experience in designing and implementing these methods for practical applications.

Specifically, students will:

  • Learn the theoretical foundations of reinforcement learning (Markov decision processes & dynamic programming).
  • Learn the algorithmic foundations of reinforcement learning (temporal difference and Monte-Carlo learning).
  • Gain experience in framing low-dimensional problems and implementing solutions using tabular reinforcement learning.
  • Learn about the motivation behind deep reinforcement learning and its relevance to high-dimensional applications, such as playing video games, and robotics.
  • Discover the state-of-the-art deep reinforcement learning algorithms such as Deep Q Networks (DQN), Proximal Policy Optimisation (PPO), and Soft Actor Critic (SAC).
  • Implement and experiment with a range of different deep reinforcement learning algorithms in Python and PyTorch, and learn how to visualise and evaluate their performance.

Role

Course Leader

Bits, Brains and Behaviours (PG) - BIOE97153

Aims

The course provides both basic and advanced knowledge in reinforcement learning across three core skills: theory, implementation, and evaluation and the underling biology. Students will learn the fundamentals of both tabular reinforcement learning and bio-inspired learning, and will gain experience in designing and implementing these methods for practical applications.

Specifically, students will:

•          Learn the theoretical foundations of reinforcement learning (Markov decision processes & dynamic programming).

•          Gain experience in framing low-dimensional problems and implementing solutions using tabular reinforcement learning.

•         Understand the links between biological brain learning and machine learning and the generation of behaviour from these.

•          Implement and experiment with a range of different reinforcement learning algorithms by implement these algorithms in software (Python or Matlab), and learn how to visualise and evaluate their performance.

Role

Lecturer

Bits, Brains and Behaviours (UG) - BIOE97157

Aims

The course focusses on how we can understand brains and behaviour from a data-driven perspective. Behaviour is the only way that brains can interact with the world. At the same time, many real-world problems that we want to tackle in artificial intelligence are about mimicking some form of intelligent human behaviour.Thus studying the link between brains and behaviour is fundamental for understanding both biology and technology, as it allows us to use engineering principles to understand how human and robot brains  learn and control behaviour. To this end we will look at biological and engineering principles of closed-loop autonomous systems and analyse or implement them in practical sessions using programming and hands on hardware. Building on their advanced mathematical and coding skills students will work together in student-led discovery or innovation oriented group project within the remit of the course. The course requires an advanced mathematical background and programming skills at a level, where the question which high-level imperative programming language is going to be used should not be relevant.  

Role

Lecturer