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CM50263: Artificial intelligence

Follow this link for further information on academic years Academic Year: 2016/7
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW60EX40
Further information on unit assessment Assessment Detail:
  • Assessment detail to be confirmed ( %)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites: Before taking this unit you must take CM50258 OR take CM50109 OR take another programming unit
Further information on descriptions Description: 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.

Learning Outcomes:
On completion of this unit, students will be able to:
1. Understanding of a wide range of AI techniques, their advantages and disadvantages.
2. Appreciate AI as a mechanism to dealing 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 in a declarative manner and programming language
5. Critically evaluate state-of-the-art AI applications

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

Content:
Intro: AI, Computer Science and Cognitive Science. Logical representations in AI. Probabilistic representations & uncertainty in AI.
Search strategies: Antagonistic (mini-max) search strategies. Dynamic programming, greedy approaches, backtracking, branch & bound.
Complexity: Computationally hard problems and how they relate to AI algorithms.
Techniques: Introduction to machine learning (possible examples: Genetic algorithms & genetic programming) Decision trees & analysis, Constraint solving and satisfiability (SAT solving). Logic Programming (under the answer set semantics). Probabilistic model structures, Bayes nets, DAGs, constraint-based reasoning.
Problems: Classic problems: board games, knapsack, travelling salesperson. Advanced problems such as: video-game design, bio-informatics, music composition, social simulation.
Some aspects will be covered in more detail than others.
Coursework implementation to be in a declarative programming language.
Further information on programme availabilityProgramme availability:

CM50263 is Optional on the following programmes:

Department of Computer Science

Notes: