MSc Artificial Intelligence & Ethics

Artificial Intelligence and, more specifically, the application of machine learning techniques to big data sets is becoming increasingly prevalent in society. There is an increasing need in the tech and public sector for graduates who can not only develop machine learning applications well but who can also understand the issues that arise in relation to such applications; and who can communicate technical and societal issues clearly.

Teaching & Learning

The MSc Responsible Artificial Intelligence will be delivered through a mixture of lectures, seminars, lab-based tutorials and office hours.

Students who are enrolled full-time should anticipate devoting approximately 35-40 hours per week to their studies for the duration of their degree. In Michaelmas and Hilary terms, this will include approximately six to seven formal contact hours per week, with the remainder consisting of structured independent study.

Part-time Study

The Master's programme can be taken part-time for over two years. Part-time students attend the same classes as their full-time colleagues, taking 50% of the course load each academic year. The classes are not run separately in the evening for part-time students.

While we try to make part-time study as flexible as possible, our Master’s programmes are demanding and we advise students that, if they intend to work alongside the course, their work should be flexible.

Timetables

Timetables are usually made available to students during Freshers’ Week. Teaching can be scheduled to take place on any day of the week. However, when possible, Wednesday afternoons are usually reserved for sports and cultural activities.

Structure

The MSc Artificial Intelligence with a Human Face consists of eight taught courses, six courses (90 credits) in Computer and Data Science and two courses (30 credits) in the Humanities, plus an individual software development project (60 credits).

The six computing courses teach students the theory and application of computer and data science, especially in relation to Artificial Intelligence (AI). They are taught in pairs, one per term. In each pair, one course always complements the teaching of its counterpart. In Michaelmas term, students learn the basics of programming (e.g. control flow statements and data collections), alongside the fundamentals of computing (e.g. logic operators, algorithm complexity and data structures). In Hilary, students learn how to ingest and transform data (e.g. numerical arrays, images or text), alongside how to design and structure programs. Finally, at Trinity students learn how to develop machine learning applications at breadth and depth. We choose Natural Language Processing to study in-depth because it has a profound technical and societal impact nowadays and it is pertinent to humanics.

The two humanities courses teach students to think carefully and communicate clearly about philosophical (ethical and other) issues arising in relation to computing, data usage, AI, and other emerging technologies.

The individual project offers an opportunity for students to pursue impactful interdisciplinary projects on an agreed topic of their choice in digital humanities or computational social sciences within the college and with its partners. It runs throughout the year so that students have ample time to focus on their independent learning with the right guidance from their supervisor(s). Students will gain experience interacting with non-technical audiences to gather problem specifications and explain solutions.

Core courses

For the MSc Artificial Intelligence with a Human Face, all students will complete the following compulsory courses.

Michaelmas Term

• Programming with Data (Level 7), 15 Credits
• Discrete Structures, 15 Credits
• AI & Data Ethics, 15 Credits

Hilary Term

• Foundations of Data Science (Level 7), 15 Credits
• Object-Oriented Design, 15 Credits
• PLUS Either:
  • Minds & Machines (Level 7), 15 Credits
• Or:
  • Technology & Human Values (Level 7), 15 Credits

Trinity Term

• Principles of Machine Learning (Level 7), 15 Credits
• Natural Language Processing (Level 7), 15 Credits
• MSc Dissertation Project, 60 Credits