ARTIFICIAL INTELLIGENCE - 2022/3
Module code: COM2028
Computers have become commonplace in many areas of our lives and are able to accomplish many things that humans would find difficult, if not impossible, to do by their own unaided efforts. Whilst computers can perform many calculations in a very short time they generally do not possess the ability to learn or to reason about novel situations or to process incomplete or uncertain data. They will need knowledge of the environment in which they operate so that they can understand what their sensors are monitoring and so that they can behave rationally. This module demonstrates the basic principles and methods of Artificial Intelligence (AI) and provides the basis for understanding and later choosing the correct tools for building such systems. Applications that motivate the development of Artificial Intelligence technology include intelligent robots, automated navigation for autonomous vehicles, object recognition and tracking, medical diagnosis, language communications and many others. Any application that requires human-like intelligence is an application for Artificial Intelligence.
The foundational artificial intelligence skills taught in this module provide students digital skills that are fundamental to many computer science problems today. This new set of algorithms allow students to build solutions to a wider class of problems. Build on the foundational material taught in COM1033 (Foundations of Computing 2) and in COM2034 (Information Retrieval), students are taught algorithms for identifying patterns in data. These are highly employable skills.
AI is currently an area that is in high demand in industry. This module teaches students to work with large and complex datasets to identify patterns. Students are equipped with practical problem-solving skills, theoretical skills, and foundational AI skills, all of which are highly valuable to employers.
Global and Cultural Skills
Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world.
Resourcefulness and Resilience
This module involves practical problem-solving skills that teach a student how to work with complex and unstructured data sets. The algorithms taught in this module such as K-nearest neighbours and Neural Networks can be applied to a wide range of different scenarios, giving students new techniques for solving problems.
TANG H Lilian (Elec Elec En)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: I400
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 106
Lecture Hours: 22
Laboratory Hours: 22
Prerequisites / Co-requisites
COM1033 Foundations of Computing II.
- Introduction to probability, Bayes’ Theorem and its applications
- Introduction to Learning
- K-Nearest Means
- K-Nearest Neighbour
- Decision Tree
- Neural Networks
- Support-Vector Machines
- Bayesian Classifiers
- Normal Distribution and Gaussian Classifiers
- Optimisation and Genetic Algorithms
- Visual Perception
- Feature Extraction
- Region Detection and Segmentation
- Classification and Pattern Recognition
- Natural Language Understanding
- Syntax, Semantics and Context Analysis
- Probabilistic Language Processing
|Assessment type||Unit of assessment||Weighting|
|Practical based assessment||LAB ASSIGNMENT||20|
|Coursework||COURSEWORK PROJECT (INDIVIDUAL)||30|
|Examination Online||ONLINE (OPEN BOOK) EXAM WITHIN 4HR WINDOW||50|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
· Ability to design and implement basic computer vision and machine learning techniques.
· Ability to explain essential elements in various machine learning and computer vision techniques.
· Ability to appraise scientific literature within the field of Artificial Intelligence
Thus, the summative assessment for this module consists of:
· Completion of weekly lab assignments.
Each lab assignment should be handed in the following Monday in order to receive timely formative feedback from the lecturer.
Any improved version of all work must be submitted soon after week 8 for an overall score (see the detailed due date on SurreyLearn).
· Coursework project report. Deadline: week 10.
· Unseen exam
Formative assessment and feedback
Between week 2-9, students will be guided to work on weekly tasks through lab exercises, which should be submitted through SurreyLearn in order to receive individual feedback. The solutions to lab tasks and questions will be available after each submission. Students will be able to complete the coursework project successfully once the foundation of the coursework is built through lab exercises. There will also be sessions in the lab targeting questions related to coursework project. Individual Feedback on the coursework project will be given as soon as possible within two weeks or before the exam whichever comes first. Students will be able to gauge their progress through these feedbacks at different stages.
- This module aims to demonstrate a variety of techniques for capturing human knowledge and represent it in a computer in a way that enables the machine to learn and reason over the data represented and mimic the human ability to deal with incomplete or uncertain data. This module introduces the range of artificial intelligence elements that future robots or intelligent machines must possess as embedded implementations if they are to behave intelligently.
|1||Understand the fundamental concepts of statistics required in order to support AI techniques.|
|2||Describe methods for acquiring and representing human knowledge.|
|3||Describe techniques for representing acquired knowledge in a way that facilitates automated reasoning over the knowledge.|
|4||Describe how AI systems are developed and work.|
|5||Demonstrate the ability to design and implement basic AI techniques.|
|6||Explain essential elements in various machine learning and computer vision techniques|
|7||Evaluate the use of machine learning and computer vision techniques, highlighting their strengths and weaknesses|
|8||Evaluate emerging AI techniques.|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
There are 12 teaching weeks including revision sessions and one reading week in Semester 2. Each week there will be
- 2 hour lectures
- 2 hour lab sessions
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: COM2028
Programmes this module appears in
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.