2022/23 Taught Postgraduate Module Catalogue

COMP5625M Deep Learning

15 creditsClass Size: 330

Module manager: Professor David Hogg
Email: d.c.hogg@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2022/23

Pre-requisites

COMP5712M

Programming for Data Science

This module is not approved as an Elective

Module summary

The module introduces the field of Deep Learning, taking a strongly integrative and state of the art approach. 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 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 Deep Learning, 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
1. Demonstrate and understanding of the fundamental concepts and methods of deep learning;

2. Apply deep learning for standard AI tasks using a state-of-the-art programming environment (e.g., Python with PyTorch or Keras);

3. Represent data from various modalities (e.g. text, images, records) for multi-modal deep learning (e.g. translating images into textual captions;

4. Critically evaluate systems, using standard performance metrics;

5. Demonstrate an understanding of the current limitations of deep learning, the dependence on data and computational resources, and the challenge of causal explanation;

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, text and structured data.
• Learning from data, performance evaluation and network visualisation.
• Deep feedforward, convolutional, recurrent and graph neural networks; generative adversarial 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.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	22	1.00	22.00
Practical	10	1.00	10.00
Private study hours			118.00
Total Contact hours			32.00
Total hours (100hr per 10 credits)			150.00

Private study

The student will design, build, train and evaluate deep neural network models. Reports from a subset of these exercises will form part of the formal assessment of the module.

Opportunities for Formative Feedback

Formative assessment will be coupled to the practical exercises in order to monitor student progress and provide feedback.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Practical Exercise - Program development with DFN/ CNN	25.00
Report	Practical Exercise - Program development multi-modality	25.00
Total percentage (Assessment Coursework)		50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Open Book exam	2 hr	50.00
Total percentage (Assessment Exams)		50.00

Resits will be assessed by open book exam only.

Reading list

The reading list is available from the Library website

Last updated: 01/06/2022 16:59:02

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