|Module Name||Artificial Intelligence|
|ECTS Weighting ||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.
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:
- 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 Component||Brief Description||Learning Outcomes Addressed||% of Total||Week Set||Week Due|
|Take-Home Examination||Real-Time Exam (time limited)||LO1, LO2, LO3,|
|Individual Assignment||Programming Assignment||LO2, LO3||20%||Week 3||Week 6|
|Group Assignment||Programming Assignment and Research Paper||LO1, LO4, LO5, LO6, LO7||30%||Week 7||Week 11|
Real-Time Examination (2.5 hours, 100%).
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||22 hours|
|Tutorial or seminar||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.
Prerequisite modules: N/A
Other/alternative non-module prerequisites: Programming proficiency required, preferably in Python.
While not required, it would be beneficial to take this module in conjunction with CS7CS4: Machine Learning.