Program Overview

Machine Learning for Robotics (COMP0245)

Key Information

The Machine Learning for Robotics module is part of the Faculty of Engineering Sciences, specifically within the Computer Science department. It carries a credit value of 15.

Faculty and Teaching Department

• Faculty: Faculty of Engineering Sciences
• Teaching department: Computer Science

Credit Value and Restrictions

• Credit value: 15
• Restrictions: Module delivery for PGT (FHEQ Level 7) available on MSc Robotics and Artificial Intelligence; MSc Systems Engineering for the Internet of Things.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

Machine learning has become an invaluable tool for robotics, applied to almost all areas of robotics, from object recognition to low-level control. The module covers general concepts such as regression, classification, density estimation, and dimensionality reduction, as well as techniques for computing intractable integrals. Algorithms are connected to real-world data problems, allowing learners to complete research-like tasks with autonomy. Learners gain an understanding of the material and main concepts/theories taught in this module, developing skills for analysis and synthesis.

The module also covers ethical considerations arising from the use of machine learning in robotics, including bias and fairness, privacy, accountability, safety, and ethical decision-making. It touches on sociotechnical topics such as employment, environment, sustainability, and regulation. Knowledge of research-informed literature is an outcome of this module, enabling learners to identify key problem areas and choose appropriate methods for their resolution.

Aims

The aims of this module are to:

• Provide students with a strong foundational understanding of machine learning, particularly for complementary and follow-up modules.
• Offer an understanding of the relevance of machine learning within the context of robotics and control.
• Support students in developing their understanding of fundamentals such as regression, classification, density estimation, dimensionality reduction, and model selection, with the goal of applying these to data-modelling problems.
• Provide an applied context for the use of fundamental concepts in object-oriented programming in the creation of programs for machine learning applications.
• Equip students with the knowledge and skills necessary to navigate the ethical complexities of machine learning and contribute to the development of AI systems that align with societal values and norms.

Intended Learning Outcomes

On successful completion of this module, a student will be able to:

1. Develop a systematic approach to analyzing data using machine learning.
2. Evaluate the quality and suitability of different machine learning methods for modelling data.
3. Examine properties of machine learning algorithms using data interpretation.
4. Develop and build on basic elements of the programming paradigm and the ability to compose these to produce programs that function as intended, scale efficiently in a multi-processor environment, and deliver machine learning results.
5. Analyze the ethical and societal implications of using machine learning in robotics and propose possible solutions to address ethical concerns.

Indicative Content

The module will typically cover:

• Linear regression
• Logistic regression and classification
• Principle component analysis
• Clustering
• Introduction to deep learning
• Feed-forward NNs and ResNets
• Backprop and autodiff
• Simple CNNs

Requisite Conditions

To be eligible to select this module as optional or elective, a student must be registered on a programme and year of study for which it is formally available.

Module Deliveries for 2026/27 Academic Year

• Intended teaching term: Term 1
• Postgraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of study: In person
• Intended teaching location: UCL East
• Methods of assessment:
  • 80% Group activity
  • 20% Viva or oral presentation
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 67
• Module leader: Mr Francisco Porto Guerra E Vasconcelos