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CS5062: MACHINE LEARNING (2019-2020)

Last modified: 25 Sep 2019 09:58


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 face detection, anomaly detection, and which 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 Aberdeen Sustained Study No
Co-ordinators
  • Dr Wei Pang
  • Dr Frank Guerin

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).


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

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


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Summative Assessments

Project and Implementation of Software/Program

Assessment Type Summative Weighting 50
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Project and Implementation of Software/Program

Assessment Type Summative Weighting 50
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Formative Assessment

There are no assessments for this course.

Resit Assessments

Resubmission of failed elements

Assessment Type Summative Weighting
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

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