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 Applied Artificial Intelligence
2. Module Code COMP534
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester Second Semester
7. CATS Level Level 7 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Dr BP Keetch Computer Science Blaine.Keetch@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

     10

     40
17.

Private Study

 110
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):

 
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
 

1. To provide students with an introduction to key topics in the field of Artificial Intelligence (AI), including Machine Learning, Deep Learning, Natural Language Processing (NLP) and Computer Vision.
2. To present fundamental problems in all these areas and explain the common methods used to deal with these problems.
3. To develop the practical skills necessary to build AI applications using data from different domains.
 
28. Learning Outcomes
 

(LO1) An in-depth understanding of key areas in applied machine learning.
 

(LO2) Ability to critically justify the use of Neural Network architectures and Deep Learning.
 

(LO3) Ability to apply state-of-the-art machine learning techniques to a variety of applications.
 

(LO4) Ability to critically evaluate the output of machine learning solutions.
 

(S1) Critical thinking and problem solving – Problem identification.
 

(S2) Critical thinking and problem solving – Critical analysis.
 

(S3) Creative thinking to develop appropriate solutions.
 
29. Teaching and Learning Strategies
 

Teaching Method 1 – Lecture
Description:
Attendance Recorded: Not yet decided
Notes: 3 lectures per week for 10 weeks

Teaching Method 2 – Laboratory Work
Description:
Attendance Recorded: Not yet decided
Notes: 1 lab session per week for 10 weeks

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
   

Machine Learning basics (2 weeks):
•Module introduction and AI / machine learning overview
•Fundamental elements: supervised and unsupervised learning, feature engineering, bias/variance issues, model performance evaluation metrics, hyperparameter tuning, etc.
•Introduction to software libraries available (e.g., pyTorch, etc.)

Neural Networks architectures and Deep Learning (2-3 weeks):
•Neural networks overview
•Deep Learning models and software libraries’ capabilities on: convolutional neural networks, recurrent neural networks, etc.
•Implementation of basic deep learning architectures for simple tasks.

Applications to Natural Language Processing (2-3 weeks):
•Natural Language Processing (NLP) overview
•Implementation of text classification or sentiment prediction methods using long-short term memory networks

Applications to Computer Vision and Image Understanding (2-3 weeks):
•Computer Vision overview
•Implementation of image classification/segmentation, object detection using convolutional networks.
 
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
   
33. CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
  (534) Assignment 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. 20 30
  (534.1) Assignment 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. 20 35
  (534.2) Assignment 3 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. 20 35