Artificial Intelligence MSc

Program Overview

Imperial College London's Artificial Intelligence MSc Program prepares STEM graduates with a strong foundation in AI fundamentals and programming. Through projects and industry collaborations, students gain practical experience applying AI in various domains, while also exploring the ethical and social implications of AI advancements. The program equips graduates with the skills needed to drive innovation and solve real-world problems in the rapidly evolving field of artificial intelligence.

Program Outline

Degree Overview:

This MSc program is designed for mathematically-minded STEM graduates, providing intensive training in programming and the fundamentals of artificial intelligence (AI). The program aims to equip students with the technical skills required for this growing field, while also exploring realistic applications through group and individual projects.

Objectives:

• Technical Skills: Develop a strong foundation in programming and AI fundamentals.
• Real-World Applications: Gain practical experience through projects, exploring the application of AI in various domains.
• Industry Connections: Forge links with major technology companies and work on industry-initiated projects.
• Ethical Considerations: Explore the ethical and legal issues arising from new developments in AI.

Core Modules:

• Introduction to Machine Learning: Covers different types of machine learning problems and basic algorithms.
• Python Programming: Focuses on the fundamentals of Python programming, including procedural, object-oriented, and functional programming.
• Introduction to Symbolic Artificial Intelligence: Explores the application of knowledge of domain properties and agent architectures in symbolic AI.
• Ethics, Fairness, and Explanation in AI: Examines ethical and philosophical issues, fairness and bias, and justifiable explanations in AI decision-making.
• Software Engineering Group Project: Provides practical experience in developing AI applications and using software engineering techniques.

Optional Modules:

• Reinforcement Learning: Covers the principles of autonomous systems learning and mathematical solutions using reinforcement learning theory.
• Computational Optimisation: Deepens understanding of optimal decision-making models, algorithms, and applications.
• Mathematics for Machine Learning: Focuses on designing and implementing modern statistical machine learning methodologies and inference mechanisms.
• Modal Logic for Strategic Reasoning in AI: Develops skills in using modal logics for knowledge representation and automated reasoning in AI.
• Logic-Based Learning: Provides in-depth understanding of logic-based learning, from foundational concepts to recent advances.
• Deep Learning: Explores fundamental concepts and advanced methodologies of deep learning and their real-world applications.
• Knowledge Representation: Examines the importance of knowledge representation and reasoning in intelligent systems.
• Probabilistic Inference: Explores the use of probability for computer decision-making, including inference networks and linear
  on-linear methods.
• Machine Learning for Imaging: Covers fundamental concepts and advanced methodologies of machine learning for imaging, with applications in computer vision and medical image analysis.
• Natural Language Processing: Acquires techniques and tools for developing NLP components and applications.
• Robot Learning: Introduces the emerging field of robot learning and how robots acquire skills using machine learning.
• Robotics: Explores mobile robotics and its applications in real-world tasks.
• AI Ventures: Examines applications of AI technologies for improving or transforming existing systems in finance, health, and other sectors.
• Computational Neurodynamics: Understands the theoretical foundations of computational neuroscience and tools for simulating brain intelligence.
• Deep Graph-Based Learning: Explores different aspects of graph theory and learning, including conventional graph data analysis and graph neural networks.
• Human-Robot Interaction: Covers the intersection of robotics and human-computer interaction, including user-centric design, data analysis, and theoretical foundations.
• Software Engineering for Machine Learning Systems: Implements and operates a simplified machine learning-based system, covering engineering concepts for trustworthy systems.

Group 2:

• Computational Finance: Covers basic concepts of quantitative finance and financial engineering, addressing decision, hedging, and pricing problems.
• Principles of Distributed Ledgers: Analyzes foundational principles of decentralized ledgers and their application in cryptocurrencies.
• Quantum Computing: Introduces the basic notions of quantum computing, including quantum bits, entanglement, and algorithms.
• Statistical Information Theory: Explores the connections between information theory, statistics, and machine learning, linking computing, statistics, and geometry.

Individual Project:

Students demonstrate independence and originality by developing a significant AI application, putting into practice techniques learned throughout the course.

Assessment:

• Assessed Coursework: 20%
• Examinations (Practical and Written): 30%
• Group and Individual Project/Internship: 50%

Teaching:

• Teaching Methods: Group projects, individual projects, seminars, tutorials, computing labs, and virtual learning environment.
• Faculty: The program is delivered by the Department of Computing.
• Unique Approaches: The program emphasizes practical experience through projects and industry collaborations.

Careers:

• Potential Career Paths: Application/web development, networking, AI, media, finance, robotics, computer games, chip design, cyber security, data management, bio-medical systems, and transport.
• Opportunities: Graduates are sought after in various sectors, including healthcare, manufacturing, and the automotive industry.
• Outcomes: The program equips students with the skills needed for industries recognizing AI's transformative potential.

Other:

• Life as a Student: The program website includes a video showcasing the experience of a current student.

Tuition Fees Home fee Home fee 2024 entry £22,250 Inflationary increases Your fee is based on the year you enter the university, not your year of study. Find out more about our tuition fees payment terms, including how inflationary increases are applied to your tuition fees in subsequent years of study. Which fee you pay Whether you pay the Home or Overseas fee depends on your fee status. This is assessed based on UK Government legislation and includes things like where you live and your nationality or residency status. Find out how we assess your fee status. Postgraduate Master's Loan For courses starting on or after 1 August 2024, the maximum amount is £12,471. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs. Overseas fee Overseas fee 2024 entry £41,750 Inflationary increases Your fee is based on the year you enter the university, not your year of study. Find out more about our tuition fees payment terms, including how inflationary increases are applied to your tuition fees in subsequent years of study. Which fee you pay Whether you pay the Home or Overseas fee depends on your fee status. This is assessed based on UK Government legislation and includes things like where you live and your nationality or residency status. Find out how we assess your fee status. Postgraduate Master's Loan For courses starting on or after 1 August 2024, the maximum amount is £12,471. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.