SSST Subjects

Artificial Intelligence


Programme(s) where module is offered

  • BSc Computer Science with Electrical Engineering;
  • BSc Computer Science with Economics;
  • BSc Computer Science with Management;
  • BSc Computer Science with International Relations;
  • BSc Computer Science with Political Science;

Status (core, option, free choice)



FHEQ Level



Unit Value



Term taught



Pre-Requisite Modules or Qualifications

MATH260, CSIS280, CS330


Module Code



Module coordinator

Dzemal Zildzic


Applicable From



Educational Aims of the Module

  • This main goal of the module is to equip students with the tools to tackle new Artificial Intelligence (AI) problems they might encounter in life.
  • This module will be a survey of the field of Artificial Intelligence and students are not expected to have any prior knowledge on the topic, but they are expected to have good programming skills.
  • Throughout the semester, students will work on a project where they will need to develop an AI application and therefore excel their programming skills.
  • Throughout the project and module itself, they will learn to identify and apply AI algorithms on a concrete problems

Module Outline/Syllabus

  • Introduction to AI
  • Uninformed search
  • A* search and Heuristics
  • Constraint Satisfaction Problems
  • Game Trees
  • Markov Decision Processes
  • Reinforcement Learning
  • Logic
  • Probability
  • Markov Models
  • Bayes’ Nets
  • Decision Diagrams
  • Advanced Applications: NLP, Games, Cars, Robotics, Computer Vision
  • Advanced topics

Student Engagement Hours

Type Number per Term Duration Total Time
Lectures 30 2 hours 60 hours
Laboratory sessions 15 2 hours 30 hours
Total Guided/Independent Learning Hours 110
Total Contact Hours 90
Total Engagement Hours 200

Assessment Method Summary

Type Number Required Duration / Length Weighting Timing / Submission Deadline
Assignment 3 1,000 words 15% Throughout semester
Mid-term exam 1 90 minutes 20% Week 8
Project 1 2,500 words 15% Week 14
Final Exam 1 180 minutes 50% End of semester

Module Outcomes

Intended Learning Outcomes:

  • Demonstrate a systematic understanding of the core concepts and principles of AI
  • Critically Analyze the structure of a given problem in a way that they can choose an appropriate paradigm in which to frame that problem.
  • Critically evaluate the significance of efficient algorithms
  • Independent learning and algorithm implementation

Teaching and Learning Strategy:

  • The planned lectures provide an overview of the technical material, and guide the acquisition of material available in the text. (ILO: 1-4)
  • Laboratory sessions, discussions and laboratory time are used to work through formal exercises and problems. (ILO:1-4)
  • Independent study is based on the recommended text (ILO: 1-4)
  • Project enables students to develop communication skills and apply what they have learnt in the module to a practical problem (ILO: 2 - 4)

Assessment Strategy:

  • Final Exam (ILO:1-3)
  • Mid-term exam (ILO:1, 2)
  • Assignment (ILO: 1-4)
  • Project (ILO: 1-4)

Practical Skills:

  • Understand and be able to recognize and apply AI algorithms in a real-world application
  • Design and build intelligent artifacts.
  • Develop an AI application (e.g., game)
  • Advanced coding of optimization algorithms

Teaching and Learning Strategy:

  • Laboratory sessions (PS: 2-4)
  • Use of midterm to test student subject knowledge (PS:1-2)
  • Project (PS:1-4)
  • Assignments (PS:1-4)

Assessment Strategy:

  • Project (PS:1-4)
  • Assignment (PS:1-4)

Transferable Skills:

  • Problem-solving skills
  • Oral and written presentation skills
  • Team work
  • Critical thinking

Teaching and Learning Strategy:

  • Laboratory sessions (TS:1-4)
  • Assignment (TS:1-4)
  • Lectures (TS:1-4)

Assessment Strategy:

  • Mid-term exam (TS:1, 4)
  • Final exam (TS:1, 4)
  • Assignment (TS:1-4)
  • Project (TS:1-4)

Key Texts and/or other learning materials

Set text

  • Russel, S., Norvig P., (2014), Artificial Intelligence: A Modern Approach, 3rd Edition. Pearson

Supplementary Materials

  • Warwick, K. (2011), Artificial Intelligence: The Basics. Routledge

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.

More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook.

The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : Autumn 2016

Date approved by School Learning and Teaching Committee: 28th September 2016

Date approved by School Board of Study : 12th October 2016

Date approved by University Learning and Teaching Committee: 2nd November 2016

Date of Annual Review: December 2017


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