CS2AINU: Artificial Intelligence

Module code: CS2AINU

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: 5

When you’ll be taught: Semester 2

Module convenor: Dr Ferran Espuny Pujol , email: f.espunypujol@reading.ac.uk

NUIST module lead: Shi Liang, email: liangshi_work@163.com

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE CS2PPNU (Compulsory)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: No

Talis reading list: No

Last updated: 24 April 2025

Overview

Module aims and purpose

Apply fundamental methods in artificial intelligence to various real-world problems. You’ll expand your critical thinking skills to solve large problems, together with your writing skills for algorithm development and software implementation.

The main aim of this module is to familiarise students with the fundamental methods in Artificial Intelligence (AI) such as adversarial search, game theory, supervised, semi-supervised and unsupervised learning, reinforcement learning and artificial neural networks (including deep learning). The application of these methods shall be demonstrated over variety of real-world problems including classification, regression, predictive modelling, information extraction, and signal (vision/speech) processing.

Students will also be able to demonstrate their abilities in:

• Critical thinking to solve a large problem integrating components of data engineering, algorithm development and implementation; and
• Professional and effective writing for algorithm development and software implementation.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Explain the foundational theory and concepts underpinning Artificial Intelligence (AI);
2. Discuss and differentiate a wide range of AI algorithms and techniques, considering their ethical implications, risks, and safety aspects;
3. Analyse critically various AI problems with their formulations and then devise appropriate solutions;
4. Employ modern tools and frameworks to address a real-world problem in a small-scale AI project and demonstrate the practical skills in the field.

Module content

• Introduction to AI: types, function, social and ethical aspects.
• Supervised learning (regression/classification)
• Missing data and data imbalance, model evaluation and performance assessment
• Linear/Logistic regression, gradient descent optimization, overfitting and feature selection (Ridge regression, regularization and LASSO)
• Shallow machine learning methods: decision trees, naïve Bayes, support vector machines, random Forests, XGBoost; ensemble methods (bagging and boosting)
• Unsupervised learning (Nearest Neighbours, K-means clustering)
• Artificial Neural Networks
• Training ANNs (batch/minibatch/stochastic gradient descent) for applications including classification, regression, prediction
• Artificial agents, adversarial search, and Game Theory
• Ethical aspects and risks/safety of AI systems

Structure

Teaching and learning methods

The module consists of 2-hour lectures and 2-hour practical sessions per week. The lectures will introduce students the theories, concepts and underpinning principles specified in the indicative content while the supervised practical sessions will guide them to develop thorough understanding in implementing AI algorithms for variety of different tasks. The formal lecture and practical sessions will enable students to apply the fundamental AI techniques to solve a given problem, by demonstrating using programming, analysis and report writing. Moreover, these sessions will be supplemented with several forms of digital resources to support learning. The summative assessment consists of one piece of individually written coursework assignment which requires every student to demonstrate his/her achievement in developing a small-scale AI solution.  

Study hours

At least 44 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Level 3 (i.e. foundation modules for Part 0) and Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Each topic in a week has defined learning tasks which will enable students to self-reflect on the learning.   

Outcomes of the formative assessment for each topic may be given in the guidance tutorial notes, online tests feedback. 

Basic algorithms will be presented in pseudo codes and/or in executable codes (e.g., Python) towards weekly studies.  

Reassessment

Additional costs