Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: CSE417
Module Title: Artificial Intelligence
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2016/17
Semester: SEM1
Originating Department: School of Advanced Technology
Pre-requisites: N/A
To equip students with a comprehensive knowledge on the principles, techniques, algorithms and applications of Artificial Intelligence.

Artificial Intelligence is a contemporary area of computing which is fundamental for a wide range of applications. This module is proposed as an option for MSc Financial Computing (and other MSc programmes where appropriate in future) to provide the students with an opportunity to broaden their knowledge and skills for the application of Artificial Intelligence techniques.

Learning outcomes 
A Demonstrate a systematic and critical understanding of Artificial Intelligence;

B Critically evaluate and apply Artificial Intelligence algorithms;

C Show expertise in the use of state-of-the art techniques for the implementation of Artificial Intelligence solutions to real world application problems.

Method of teaching and learning 
Students are expected to attend a two- hour formal lecture and either a two-hour tutorial or a two-hour lab session in a typical week. Lectures deliver the contents specified in the syllabus to the students. Tutorials/labs expand their understanding of lecture materials and equip them with necessary programming skills.

In addition, students are expected to devote the required number of hours as unsupervised / private studies for reflection of lecture materials and reading and practical work. Two practical coursework assignments are used to assess their understanding of the lecture materials and their capability in solving practical problems. A written examination at the end of the module assesses their overall academic achievement.
Week 1: Overview and history of Artificial Intelligence

Weeks 2-3: Search strategies: uniformed search strategies, informed (heuristic) search strategies, examples and advanced applications

Weeks 4-5: Programming Languages (e.g. Prolog): programming fundamentals, solving logic problems, building basic data structures, implementing search strategy, etc.

Weeks 5-6: Knowledge Representation and Expert Systems: logic fundamentals, forward and backward chaining in rule-based systems, generating explanation, introducing uncertainty, examples and advanced applications

Weeks 7-8: Uncertain knowledge and Reasoning: e.g. probabilities, beliefs and Bayesian networks, probabilistic reasoning in Bayesian networks, examples and advanced applications

Weeks 9-10: Learning Algorithms: supervised learning, unsupervised learning, examples and advanced applications

Weeks 11-12: Natural Language Processing: e.g. language models, text classification, information retrieval, information extraction, phrase structure grammars, syntactic analysis, augmented grammars and sematic interpretation, machine translation, and speech recognition, examples and advanced applications

Week 13: Revision

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26    13  13    98  150 


Sequence Method % of Final Mark
1 Practical Task 15.00
2 Practical Task 15.00
3 Written Examination 70.00

Module Catalogue generated from SITS CUT-OFF: 8/24/2019 3:35:18 PM