Module Specification

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
1. Module Title Advanced Artificial Intelligence
2. Module Code COMP219
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 5 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Professor X Huang Computer Science Xiaowei.Huang@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 30

    5

    35
17.

Private Study

115
18.

TOTAL HOURS

150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

COMP122 Object-Oriented Programming; COMP116 Analytic Techniques for Computer Science; COMP111 Introduction to Artificial Intelligence
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:

25. Programme(s) (including Year of Study) to which this module is available on a required basis:

26. Programme(s) (including Year of Study) to which this module is available on an optional basis:

27. Aims
 

• To equip students with the knowledge about basic algorithms that have been used to enable the AI agents to conduct the perception, inference, and planning tasks;
• To equip students with the knowledge about machine learning algorithms;
• To provide experience in applying basic AI algorithms to solve problems;
• To provide experience in applying machine learning algorithms to practical problems.

 
28. Learning Outcomes
 

(LO1) Ability to explain in detail how the techniques in the perceive-inference-action loop work.

 

(LO2) Ability to choose, compare, and apply suitable basic learning algorithms to simple applications.

 

(LO3) Ability to explain how deep neural networks are constructed and trained, and apply deep neural networks to work with large scale datasets.

 

(LO4) Understand probabilistic graphical models, and is able to do probabilistic inference on the probabilistic graphical models.

 

(S1) Self-management (readiness to accept responsibility (i.e. leadership), flexibility, resilience, self-starting, appropriate assertiveness, time management, readiness to improve own performance based on feedback/reflective learning.)

 

(S2) Positive attitude (A 'can-do' approach, a readiness to take part and contribute; openness to new ideas and a drive to make these happen. Employers also value entrepreneurial graduates who demonstrate an innovative approach, creative thinking, bring fresh knowledge and challenge assumptions.)

 

(S3) Application of numeracy (manipulation of numbers, general mathematical awareness and its application in practical contexts (e.g. measuring, weighing, estimating and applying formulae))

 

(S4) Computer Science practice

 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided
Notes: 3 per week during semester

Teaching Method 2 - Laboratory Work
Description:
Attendance Recorded: Not yet decided

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Work
Description: On-campus synchronous sessions

 
30. Syllabus
   

Introduction (2 lectures): Introduction to the module and the Overview of Machine Learning.

Learning Basics (4 lectures): Learning Basics, Probability Foundation, Linear Algebra and Python

Traditional Machine Learning Algorithms (10 lectures): Decision Tree, k-nearest neighbour, Linear Regression, Gradient Descent, Naïve Bayes

Deep Learning Algorithms (6 lectures): Functional View, Features, Forward and Backward Training, Convolutional Neural Networks, Tensorflow, Model Evaluation

Probabilistic Graphical Models (6 lectures): Introduction, I-Maps, Reasoning Patterns, D-Separation, Structural Learning

Revision (1 lecture)

Advanced Topics (2 lectures, optional)

 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment

32. EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (219) Final Exam There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :1 0 70
33. CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (219.1) Assignement 1 Coding: Simple Machin Learning Model. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Week 6 0 15
  (219.2) Assignment 2 Coding: Train Deep Learning Agents. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Week 12 0 15