CSMAI21-Artificial Intelligence and Machine Learning

Module Provider: Computer Science
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Yevgeniya Kovalchuk
Email: y.kovalchuk@reading.ac.uk

Type of module:

Summary module description:

The main goal of this module is to familiarise students with fundamental methods in Artificial Intelligence and Machine Learning such as supervised, unsupervised, reinforcement and deep learning. The students will learn how to apply these methods to real-life problems.

Aims:

The aim of the module is to introduce students to current methods in artificial intelligence and machine learning.

Assessable learning outcomes:

Students will be able to:

• Understand the classic and fundamental algorithms of artificial intelligence and the modern machine learning methods, including shallow and deep Artificial Neural Networks.


• Acquire knowledge of artificial intelligence techniques such as problem solving, search, reasoning, planning, learning, and perception.


• Determine appropriate machine learning methods for supervised and unsupervised problems.


• Understand and apply the process of training and making predictions with neural networks.


• Determine the appropriate neural network architecture for a particular problem.


• Apply multiple classes of neural networks to real world problems involving images and text.

Additional outcomes:

Students will gain familiarity with modern machine learning and neural networks libraries with hands-on activities.

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 weekly guided practical classes that implement methods covered in the lectures.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		20
Practicals classes and workshops		10
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)		20
Ìý Ìý Wider reading (directed)		20
Ìý Ìý Advance preparation for classes		30
Ìý Ìý Preparation for tutorials		20
Ìý Ìý Preparation of practical report		30
Ìý Ìý Carry-out research project		20
Ìý Ìý Essay preparation		20
Ìý Ìý Reflection		10
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	200	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written exam	50
Set exercise	50

Summative assessment- Examinations:

One 2-hour examination paper in April/May/June.

Summative assessment- Coursework and in-class tests:

One project-based assignment.

Formative assessment methods:

Feedback provided during practical classes.

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy £Penalties for late submission for Postgraduate Flexible programmes£, which can be found here: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
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.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
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 50% overall.

Reassessment arrangements:

One 3-hour examination paper in August/September. 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).Ìý

Additional Costs (specified where applicable):

1) Required text books:ÌýÌý

2) Specialist equipment or materials:ÌýÌý

3) Specialist clothing, footwear or headgear:ÌýÌý

4) Printing and binding:ÌýÌý

5) Computers and devices with a particular specification:ÌýÌý

6) Travel, accommodation and subsistence:ÌýÌý

Last updated: 22 September 2022