Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 2

To extend the student's knowledge of machine learning acquired through module ICE3701 (Machine Learning). To introduce them to modern-day challenges and solutions. Challenges include imbalanced data, streaming data and concept drift. Solutions include Classifier ensembles and Deep Learning neural networks.

Indicative content includes:

• Review of the fundamentals of machine learning: supervised and unsupervised learning, classifier models, feature selection.
• Classifier ensembles.
• Imbalanced data, streaming data and concept drift: machine learning solutions.
• Introduction to Deep Learning neural networks and their applications.

threshold

Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

1. Apply (by hand) some of the learned methods to toy problems.

2. Understand the challenges of modern data and the potential of machine learning to address them.

3. Apply some of the learned methods to solve toy problems and real-life problems.

4. Demonstrate knowledge of advanced machine learning methods at an algorithmic level.

Type	Name	Description	Weight
	Class Test		20.00
	Lab report		20.00
	Lab report		60.00

		Hours
Laboratory	2 hours of labs x 12 weeks.	24
Lecture	2 hours of lectures x 12 weeks	24
Private study	The students will revise the material given in the lectures, prepare for the class test, and prepare the lab reports.	152

• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
• Exploring - Able to investigate, research and consider alternatives
• Information retrieval - Able to access different and multiple sources of information
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.

• Solve problems logically and systematically;
• Access and synthesize information and literature sources;
• Analyse and display data using appropriate methods and mathematical techniques;
• Demonstrate familiarity with relevant subject specific and general computer software packages.
• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Knowledge and understanding of mathematical principles

Resource implications for students

N/A

Reading list

N/A

Compulsory in courses:

• H118: BSc Data Science & Artificial Intelligencetellig year 3 (BSC/DSAI)
• H113: BSc Data Science and Machine Learning year 3 (BSC/DSML)