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CS5062: MACHINE LEARNING (2022-2023)

Last modified: 31 Jul 2023 11:19

Course Overview

This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence. 

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
  • Dr Dewei Yi
  • Dr Mingjun Zhong

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

  • Either Any Postgraduate Programme or Master of Engineering in Computing Science
  • Either Any Postgraduate Programme or Programme Level 5

What other courses must be taken with this course?


What courses cannot be taken with this course?

  • CS551U Machine Learning (Studied)
  • CS551V Machine Learning (Studied)

Are there a limited number of places available?


Course Description

This course will present advanced Machine Learning principles with applications. Practical will help students to understand principles applying to real-world applications using cutting-edge tools and libraries. The lectures will cover supervised learning, unsupervised learning, and as well as reinforcement learning. This will include regression and classification models, stochastic gradient descent algorithms, automatic differentiation, deep neural networks, model assessment and selection, model evaluation, generative adversarial networks, reinforcement learning, unsupervised learning models.

Contact Teaching Time

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

Teaching Breakdown

More Information about Week Numbers

Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

2x Project & Implementation of Software/Program (50% each) (100% in total)


Resit Assessment

Resubmission of failed elements (pass marks carried forward).

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
FactualRememberILO’s for this course are available in the course guide.

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