Module code: COM3025

Module Overview

Deep learning has shown its success in many areas including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. Deep learning is a particular kind of machine learning technique that allows computer systems to improve with experience and data, and achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts. In recent years, deep learning has seen tremendous growth in its popularity and usefulness, due in large part to more powerful computers, larger datasets and techniques to train deeper networks.

This module introduces a wide range of deep learning and other state of art techniques in AI for solving real world problems. Basic concepts on statistics and applied maths that thread through key elements in machine learning techniques will be discussed throughout the module. Students will study how to build suitable AI systems that can operate in complicated, real-world environments. The module also prepares students to explore further challenges and opportunities to improve deep learning and AI and bring them to new frontiers.

Module provider

Computer Science

Module Leader

TANG H Lilian (Elec Elec En)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 96

Laboratory Hours: 22

Guided Learning: 10

Captured Content: 22

Module Availability

Semester 2

Prerequisites / Co-requisites

COM2028 Introduction to Artificial Intelligence; background in Linear algebra and probability will be helpful.

Module content

  • Vectors and Matrices

    • Linear Algebra Basics

    • Data Representation

    • Non-Negative Matrix Factorization

    • Application Examples: Collaborative Filtering, Finding Similar Users, Recommending Items

  • Data Visualisation

    • Principle component analysis (PCA)

    • Mutidimensional Scaling

    • t-Distributed Stochastic Neighbour Embedding (t-SNE)

    • Application Examples: Visualising High Dimensional Data

  • Machine Learning Basics

    • Learning Algorithms

    • Overfitting and Underfitting

    • Hyperparameter and Cross Validation

    • Linear Regression and Stochastic Gradient Decent

  • Convolutional Neural Network (CNN)

    • CNN architecture

    • Convolution Layer, Pooling and Fully Connected Layers

    • Activation Functions

    • Application Examples: Object Recognition in Visual Data, Search Engine

  • Recurrent neural networks (RNN)

    • Deep RNN

    • Long Short Term Memory (LSTM)

    • Application examples: Generating Text, Image Captioning, Speech Recognition, sentiment analysis

  • Autoencoder and Restricted Boltzmann Machines

    • Denoising Autoencoder

    • Deep Boltzmann Machines and Deep Belief Networks

    • Application Examples: Dimensionality Reduction, Classification, Collaborative Filtering, 

  • Reinforcement Learning (RL)

    • Key Elements in Reinforcement Learning

    • Deep Reinforcement Learning

    • Application Examples: Stunt Manoeuvres in a Helicopter, Play Chess, Manage an Investment Portfolio, Make a humanoid robot walk, Play many different Atari games better than humans

Current Research

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 80

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.


Thus, the summative assessment for this module consists of:

  • Completion of lab assignments.

    Deadlines: All lab assignments are due in week 8 or during the Easter break. However, students are encouraged to submit individual assignment regularly to SurrreyLearn in order to receive timely feedback from the lecturer. A diagnostic test based on all lab assignments will be available on SurreyLearn in week8. Students will have a minimum 10 days to complete the test.  See the detailed dates on SurreyLearn.

  • Coursework project: deliverables will be in the form of a poster and the demonstration of the developed work. Due: week 10 - week 12.

  • Unseen exam


Formative assessment and feedback:

Between week 1-7, students will be guided to work on lab exercises and are encouraged to submit their work regularly through SurreyLearn in order to receive individual feedback. Students will be able to complete the coursework project successfully once the foundation of the coursework is built through lab exercises. The discussion on coursework will start from week 5 leading up to its completion date. Individual Feedback on the coursework project will be given as soon as possible within two weeks or before the exam whichever is sooner. Students will be able to gauge their progress through these feedbacks at different stages.


Module aims

  • The module aims to show students the state of art in the field of Artificial Intelligence. It will cover a variety of approaches in the context of deep learning and demonstrate how to build intelligence systems for various practical applications.

Learning outcomes

Attributes Developed
001 Understand various machine learning algorithms and mathematical methods for processing and interpreting the data KC
002 Demonstrate adequate skills in developing applications and implementing functions using the algorithms discussed in the module. PT
003 Critically evaluate existing artificial intelligence methods within the context of current trends. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The module will develop analytical skills and the understanding of the subject area through:

• Lectures

• tutorials

• In-class discussion

The module will develop practical skills through:

• Lab sessions

• Coursework

All activities will be co-ordinated via the module webpage on the Surrey Learn.

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.

Reading list
Upon accessing the reading list, please search for the module using the module code: COM3025

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computer Science BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Computing and Information Technology BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module

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 2021/2 academic year.