| Module Code | CS7IS2 |
| Module Name | Artificial Intelligence |
| ECTS Weighting [1] | 5 ECTS |
| Semester Taught | Semester 2 |
| Module Coordinator/s | Dr. Ivana Dusparic |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Appreciate the scope, applications and limitations of artificial intelligence;
- Comprehend and apply search, reasoning and planning strategies;
- Develop intelligent systems that handle uncertainty;
- Choose and use appropriate AI techniques for various kinds of problems;
- Apply knowledge search, CSP, MDP, learning techniques to real-world problems;
- Gain experience both in working as an individual and in a team on designing and developing solutions utilizing the most appropriate AI techniques;
- 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;
- Constraint 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 Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| Individual Assignment | Programming Assignment | LO2, LO3 | 35% | Week 3 | Week 6 |
| Individual Assignment | Programming Assignment | LO4, LO5 | 35% | Week 8 | Week 11 |
| Group Assignment | Research Paper | LO1, LO4, LO5, LO6, LO7 | 30% | Week 5 | Week 12 |
Reassessment Details
Individual Assignment (Including programming and research paper components) – 100%.
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 22 hours |
| Lecture | 22 hours |
| Laboratory | 0 hours |
| Tutorial or seminar | 0 hours |
| Other | 0 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 Hours | 116 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.