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CS5062: MACHINE LEARNING (2018-2019)

Last modified: 22 May 2019 17:07


Course Overview

This course presents the fundamental as well as the most popular Machine Learning theories and algorithms, used in a wide range of applications such as classification, prediction, regression, and those are core to the design of for instance computer Go player AlphaGo. This course provides the building blocks for understanding and using Machine Learning techniques and methodologies and prepares students to work in data science and general AI systems.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Old Aberdeen Sustained Study No
Co-ordinators
  • Dr Wei Pang
  • Dr Chenghua Lin

What courses & programmes must have been taken before this course?

  • Either Any Postgraduate Programme (Studied) or Master of Engineering in Computing Science

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

The course will present the theory and practice of Machine Learning, including the state-of-the-art in tools, libraries, techniques and environments. Lectures will cover key concepts, mechanisms and results, with exercises in practicals/tutorials for individuals and teams to explore practical aspects. This will include: Machine learning problems (e.g. clustering, classification, concept/model learning); Symbolic machine learning (e.g. Support vector machines, reinforcement learning, inductive and analytical learning); Statistical machine learning (e.g regression, Bayesian learning, parametric density estimation); Bio-inspired learning (e.g. Neural nets & deep learning, evolutionary computing).


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 10 Lectures during University weeks 13 - 14
  • 5 Practicals during University weeks 13 - 14

More Information about Week Numbers


Summative Assessments

Individual Project (50%); Individual Project (50%).

Resit: where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).

Formative Assessment

There are no assessments for this course.

Feedback

Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.

Course Learning Outcomes

None.

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