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Course & Unit Catalogues

CM50263: Artificial intelligence

[Page last updated: 23 October 2023]

Academic Year: 2023/24
Owning Department/School: Department of Computer Science
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Semester 2
Assessment Summary: CW 25%, EX 75%
Assessment Detail:
  • Set Exercises (CW 25%)
  • Written Examination (EX 75%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must take CM50258 OR take CM50109 OR take another programming module
Learning Outcomes: On completion of this unit, students will be able to:
1. Understand a wide range of AI techniques, their advantages and disadvantages.
2. Appreciate AI as a mechanism to deal with computationally hard problems in a practical manner.
3. Understand the concepts of formal AI and put them into practice.
4. Write small to medium sized programs for aspects of Artificial Intelligence.
5. Critically evaluate state-of-the-art AI applications.

Aims: To present a detailed introduction to formal artificial intelligence. To establish a practical understanding of intelligence and computation as strategies for problem solving, and the nature of the problems amenable to various established strategies and approaches.

Skills: Use of IT (T/F,A) Problem solving (T/F,A),Communication (T/F,A), Critical thinking (T/F,A)

Content: Goals and foundations of AI.
Problem solving (uninformed, heuristic, and adversarial search; constraint satisfaction).
Logical reasoning (propositional logic, first-order logic, logic programming).
Probabilistic reasoning (probability models, Bayesian networks).
Machine learning (possible topics include decision trees, nearest-neighbor methods, reinforcement learning, neural networks, support vector machines, boosting).
State-of-the-art AI applications will be discussed throughout the unit.

Course availability:

CM50263 is Optional on the following courses:

Department of Computer Science
  • RSCM-AFM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
  • RSCM-APM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM51 : MRes Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM52 : MSc Accountable, Responsible and Transparent Artificial Intelligence
  • TSCM-AFM39 : MSc Computer Science
  • TSCM-AFM48 : MSc Machine Learning and Autonomous Systems
  • TSCM-AWM48 : MSc Machine Learning and Autonomous Systems
Department of Electronic & Electrical Engineering


  • This unit catalogue is applicable for the 2023/24 academic year only. Students continuing their studies into 2024/25 and beyond should not assume that this unit will be available in future years in the format displayed here for 2023/24.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
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