CS7IS2 – Artificial Intelligence

Module CodeCS7IS2
Module NameArtificial Intelligence
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 2
Module Coordinator/s  Dr. Ivana Dusparic

Module Learning Outcomes

On successful completion of this module, students will be able to:

  1. Appreciate the scope, applications and limitations of artificial intelligence;
  2. Comprehend and apply search, reasoning and planning strategies;
  3. Develop intelligent systems that handle uncertainty;
  4. Choose and use appropriate AI techniques for various kinds of problems;
  5. Apply knowledge search, CSP, MDP, learning techniques to real-world problems;
  6. Gain experience both in working as an individual and in a team on designing and developing solutions utilizing the most appropriate AI techniques;
  7. Gain experience in communicating their AI-based solutions through writing, demonstrations and presentations.

Module Content

This module aims to provide students with a thorough overview of the artificial intelligence techniques and algorithms that underlie intelligent systems and an ability to apply these techniques to real-world problems.

Specific topics addressed in this module include:

  • Search;
  • Problem solving;
  • Control satisfaction problems;
  • Markov Decision Process;
  • Representing and reasoning with uncertainty;
  • Learning, including reinforcement learning;
  • Intelligent agents and multi agent systems;
  • Real-world applications.

Teaching and Learning Methods

Lectures, individual assignments, group assignments.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
Exam2-hour anonymous in-person exam LO1, LO2, LO3,
LO4, LO5
50%N/AN/A
Individual AssignmentProgramming AssignmentLO2, LO320%Week 3Week 6
Group AssignmentProgramming Assignment and Research PaperLO1, LO4, LO5, LO6, LO730%Week 7Week 11

Reassessment Details

2-hour anonymous in-person exam – 100%.

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:22 hours
Lecture22 hours
Laboratory0 hours
Tutorial or seminar0 hours
Other0 hours
Independent Study (outside scheduled contact hours), broken down by:94 hours
Preparation for classes and review of material (including preparation for examination, if applicable)36 hours
Completion of assessments (including examination, if applicable)58 hours
Total Hours116 hours

Recommended Reading List

  • Stuart Russell and Peter Norvig Artificial Intelligence: A Modern Approach, (3rd Edition) 2015 or (4th Edition) 2019. Upper Saddle River (NJ): Prentice Hall.

Module Pre-requisites

Prerequisite modules: N/A

Other/alternative non-module prerequisites: Programming proficiency in Python required.

Module Co-requisites

While not required, it would be beneficial to take this module in conjunction with CS7CS4: Machine Learning.

Module Website

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