Imperial College London

DrAntoineCully

Faculty of EngineeringDepartment of Computing

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8204a.cully Website

 
 
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Location

 

354ACE ExtensionSouth Kensington Campus

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Summary

 

Robot Learning - COMP70067

Aims

Robot Learning is an exciting new field, which studies how physical robots can learn skills using machine learning techniques, and can be seen as an "advanced reinforcement learning" module. The module first motivates the need for robot learning, by describing classical robot control methods and their limitations. Then, the module explains how reinforcement learning can be applied to physical robots acting in the real world. Finally, the module explores how robots can learn new skills by observing and interacting with humans. Lab sessions and courseworks teach students how to implement these methods in Python for a simulated robot learning to solve tasks, which culminates in a fun live competition in the final lecture.

The module assumes knowledge of the Reinforcement Learning module in the previous term, so taking Reinforcement Learning is strongly recommended, unless students have already taken a similar module elsewhere. For example, the module assumes familiarity with Markov decision processes, Q-learning, and deep reinforcement learning, although these will be briefly recapped

Role

Course Leader

Introduction to Machine Learning - COMP97101

Aims

Machine learning aims to automatically create prediction models and make decisions by leveraging data. The current applications of machine learning range from speech recognition to autonomous cars. New applications continue to appear every day. This Introduction to Machine Learning module aims to provide an overview of the different types of problems that exist in machine learning and the basic algorithms used to address them. In particular, this module covers the fundamental knowledge required to comprehend the advanced methods needed for specialised modules.

Role

Course Leader

Robot Learning - COMP97157

Aims

Robot Learning is an exciting new field, which studies how physical robots can learn skills using machine learning techniques, and can be seen as an "advanced reinforcement learning" module. The module first motivates the need for robot learning, by describing classical robot control methods and their limitations. Then, the module explains how reinforcement learning can be applied to physical robots acting in the real world. Finally, the module explores how robots can learn new skills by observing and interacting with humans. Lab sessions and courseworks teach students how to implement these methods in Python for a simulated robot learning to solve tasks, which culminates in a fun live competition in the final lecture.

The module assumes knowledge of the Reinforcement Learning module in the previous term, so taking Reinforcement Learning is strongly recommended, unless students have already taken a similar module elsewhere. For example, the module assumes familiarity with Markov decision processes, Q-learning, and deep reinforcement learning, although these will be briefly recapped

Role

Course Leader

Introduction to Machine Learning - COMP97151

Aims

This module aims to provide students with a fundamental understanding of core machine learning ideas and concepts. It introduces to students different machine learning problems and basic algorithms used to address these problems. The module will cover fundamental machine learning knowledge required to tackle more advanced, specialised modules.

Role

Course Leader

Introduction to Machine Learning - COMP70050

Aims

This module aims to provide students with a fundamental understanding of core machine learning ideas and concepts. It introduces to students different machine learning problems and basic algorithms used to address these problems. The module will cover fundamental machine learning knowledge required to tackle more advanced, specialised modules.

Role

Course Leader

Introduction to Machine Learning - COMP97162

Aims

This module aims to provide students with a fundamental understanding of core machine learning ideas and concepts. It introduces to students different machine learning problems and basic algorithms used to address these problems. The module will cover fundamental machine learning knowledge required to tackle more advanced, specialised modules.

Role

Course Leader