2024/25 Taught Postgraduate Module Catalogue

OCOM5203M Deep Learning

15 creditsClass Size: 200

Module manager: Dr Nabi Omidvar
Email: M.N.Omidvar@leeds.ac.uk

Taught: 1 Nov to 31 Dec View Timetable

Year running 2024/25

Pre-requisite qualifications

N/A

Pre-requisites

OCOM5100M

Programming for Data Science

Module replaces

N/A

This module is not approved as an Elective

Module summary

The module introduces the field of Artificial Intelligence (AI), taking a strongly integrative and state of the art approach based on deep neural networks. In line with the use of AI in key sectors (e.g. finance, health, law), there is an emphasis on the combination of multiple input modalities – specifically, combining images, text and structured data. Students gain hands-on experience in developing AI systems to address real-world problems, providing the knowledge and skills necessary to develop an AI system as part of an MSc project.

Objectives

To provide students with a state of the art understanding of AI, and highly practical skills and expertise in the construction of AI systems, including those that integrate multiple modalities.
To prepare students for a research degree involving innovation in the construction of AI systems.
To prepare students for a role in industry or the public-sector, where they may help to build, specify, recommend and critique AI systems.
To provide sufficient knowledge for students to be able to think creatively about possible AI solutions to real-world problems.
To gives students an understanding of the limitations of the state of the art and future research directions, including the need for causal explanations.

Learning outcomes
On completion of this module, students should be able to:

1. Design and implement AI systems in a language such as Python, based on machine learning;
2. Apply deep learning in standard AI tasks (e.g. image classification, sentiment analysis);
3. Represent data from various modalities (e.g. text, images, records) for multi-modal deep learning (e.g. translating images into textual captions, translating text into cognitive categories such as human sentiment);
4. Critically evaluate systems, using standard performance metrics;
5. Demonstrate a detailed understanding of the technical aspects of deep learning, including the different kinds of architecture, limitations, dependence on data and computational requirements;
6. Apply their knowledge to address challenges from a specific sector; for example in detecting financial fraud from transaction data, and in informing medical diagnosis through combining medical images and structured data from electronic patient records.

Syllabus

Indicative content for this module includes:

- Use of Python for implementing neural networks.
- Representations for images, audio, text and structured data.
- Learning from data, performance evaluation and network visualisation.
- Deep feed-forward neural networks, convolutional neural networks, autoencoder networks.
- Handling sequential and relational data.
- Hands-on application of deep learning for the analysis of images, text and structured data, and combinations of modalities.
- Hands on application of deep learning with data and tasks from a specific domain (e.g. health, finance).

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	6	1.00	6.00
Group learning	6	2.00	12.00
Independent online learning hours			28.00
Private study hours			104.00
Total Contact hours			18.00
Total hours (100hr per 10 credits)			150.00

Private study

Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.

Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and activities in the VLE.

Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow self and peer assessment providing opportunities for formative feedback from peers and tutors. 

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Online Assessment	Online Test	25.00
Practical	Program Development	75.00
Total percentage (Assessment Coursework)		100.00

The module will be reassessed by a 100% individual assessment which assesses all learning outcomes.

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:18:45

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