Introductory description
This module will introduce the foundational concepts in artificial intelligence. This module is only available to students in the second year of their degree and is not available as an unusual option or to students in other years of study.
Module aims
This module will introduce the foundational concepts in artificial intelligence. Specifically, it will provide a broad coverage of rational agency, search techniques, knowledge representation and planning, constraint satisfaction problem solving, supervised and unsupervised machine learning, reinforcement learning, and Bayesian approaches to artificial intelligence.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
- Introduction
- Rational Agents
- Uninformed and Heuristic Search
- Constraint Satisfaction Problems and Local Search
- Adversarial Search
- Knowledge Representation and Planning
- Supervised Machine Learning
- Bayesian AI
- Unsupervised Machine Learning
- Reinforcement Learning
Learning outcomes
By the end of the module, students should be able to:
- Develop an appreciation of AI through the concept of rational agency
- Understand various methods for search (uninformed, heuristic and adversarial) and constraint satisfaction problems
- Understand a variety of methods for knowledge representation along with basic reasoning and planning approaches
- Develop an appreciation of machine learning, including supervised and unsupervised methods
- Understand various methods for representing and reasoning under uncertainty
Indicative reading list
Generic Reading lists can be found in Talis
Specific reading list for the module can be found on
Subject specific skills
- Develop an appreciation of AI through the concept of rational agency
- Understand various methods for search (uninformed, heuristic and adversarial) and constraint satisfaction problems
- Understand a variety of methods for knowledge representation along with basic reasoning and planning approaches
- Develop an appreciation of machine learning, including supervised and unsupervised methods
- Understand various methods for representing and reasoning under uncertainty.
Transferable skills
Programming
Communication skills (written)
Problem solving
Critical thinking
Study time
| Type |
Required |
| Lectures |
30 sessions of 1 hour (20%)
|
| Seminars |
7 sessions of 1 hour (5%)
|
| Private study |
113 hours (75%)
|
| Total |
150 hours |
Private study description
Required reading (as identified in lectures)
Background reading
Exercise sheets
Revision
Coursework
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D5
|
Weighting |
Study time |
Eligible for self-certification |
|
Coursework
|
20%
|
|
Yes (extension)
|
|
|
Centrally-timetabled examination (On-campus)
|
80%
|
|
No
|
|
CS255 exam
- Answerbook Pink (12 page)
- Students may use a calculator
|
Assessment group R2
|
Weighting |
Study time |
Eligible for self-certification |
|
In-person Examination - Resit
|
100%
|
|
No
|
|
CS255 resit exam
- Answerbook Pink (12 page)
- Students may use a calculator
|
Feedback on assessment
Mark and written feedback returned via Tabula.
Past exam papers for CS255
Courses
This module is Optional for:
-
Year 2 of
UCSA-I1N1 Undergraduate Computer Science with Business Studies
-
Year 2 of
UCSA-G406 Undergraduate Computer Systems Engineering
-
Year 2 of
UCSA-G408 Undergraduate Computer Systems Engineering
-
Year 2 of
USTA-G305 Undergraduate Data Science (MSci) (with Intercalated Year)
This module is Option list A for:
-
Year 2 of
UCSA-G500 Undergraduate Computer Science
-
UCSA-G503 Undergraduate Computer Science MEng
-
Year 2 of
G500 Computer Science
-
Year 2 of
G503 Computer Science MEng
-
Year 2 of
USTA-G302 Undergraduate Data Science
This module is Option list B for:
-
Year 2 of
UCSA-G4G1 Undergraduate Discrete Mathematics
-
UCSA-G4G3 Undergraduate Discrete Mathematics
-
Year 2 of
G4G1 Discrete Mathematics
-
Year 2 of
G4G3 Discrete Mathematics