CS3AI18NU-Artificial Intelligence
Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:6
Semesters in which taught: Semester 1 module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4
Module Convenor: Dr Muhammad Shahzad
Email: m.shahzad2@reading.ac.uk
NUIST Module Lead: Shi Liang
Email: liangshi_work@163.com
Type of module:
Summary module description:
The main goal of this module is to familiarise students with fundamental methods in Artificial Intelligence such as supervised, unsupervised, reinforcement and deep learning. The students will learn how to apply these methods to real-life problems.
The module aims to provide students with theoretical and practical knowledge of Artificial Intelligence from various techniques and applications.
Aims:
The main goal of the module is to equip students with the knowledge of Artificial Intelligence algorithms and techniques to tackle real-world problems (Artificial Intelligence applications) such as predictive modeling, image and speech processing, web search, autonomous systems, games and robotics. Moreover, the module will provide an opportunity for students to develop their Python skills and apply that to Artificial Intelligence through practical assignments.
Assessable learning outcomes:
By the end of the module, students should be able to:
- understand the basic algorithms and techniques of artificial intelligence;
- apply state-of-the-art artificial intelligence algorithms and methods to real-world problems to create a small-scale AI project.
Additional outcomes:
Improved programming skills and applied AI through practical work.
Outline content:
- Nature and goals of artificial intelligence, its application areas
- Training machine learning models
- Natural language processing
- Image processing
- Deep learning
- Reinforcement learning
Brief description of teaching and learning methods:
The module consists of lectures and tutorials throughout the term.
Semester 1 | Semester 2 | |
Lectures | 10 | |
Seminars | 10 | |
Guided independent study: | ||
Other | 80 | |
Total hours by term | 100 | 0 |
Total hours for module | 100 |
Method | Percentage |
Written exam | 50 |
Set exercise | 50 |
Summative assessment- Examinations:
One 1.5-hour examination paper in Dec/Jan.
Summative assessment- Coursework and in-class tests:
One project-based assignment (50%).
Formative assessment methods:
Formative feedback on the project as it's being developed during the lab sessions.
Penalties for late submission:
The Support Centres will apply the following penalties for work submitted late:
- where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
- where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.
Assessment requirements for a pass:
A mark of 40% overall.
Reassessment arrangements:
One 2-hour examination paper in the NUIST reassessment period. Note that the resit module mark will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (50% exam, 50% coursework).
Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:
Last updated: 11 May 2023
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.