Differentiable Programming and Deep Learning

Module overview

Deep learning and differentiable programming has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of cheap GPGPU compute coupled with large datasets and some neat algorithms. This module will look at how deep learning works, from the theoretical foundations of the concepts of differentiable programming right through to practical implementation.

Linked modules

Prerequisites: COMP3223 or COMP6245

Aims and Objectives

Learning Outcomes

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

Apply existing deep learning models to real datasets
Gain facility in working with differentiable programming and deep learning libraries in order to create and evaluate differentiable programmes and deep networks

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Underlying mathematical and algorithmic principles of deep learning
The key factors that have made deep learning successful for various applications

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Critically appraise recent scientific literature in deep learning
Critically appraise the merits and shortcomings of model architectures on specific problems

Learning and Teaching

Teaching and learning methods

Lectures, labs and guided self-study

Study time
Type	Hours
Completion of assessment task	60
Wider reading or practice	46
Specialist Laboratory	20
Lecture	24
Total study time	150

Resources & Reading list

Textbooks

Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016). Deep Learning.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Group project report	40%
Blackboard quizzes	20%
Laboratory Report	40%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External