MACHINE LEARNING AND DATA MINING - 2019/0

Module Overview

Machine Learning and Data Mining incorporate a large number of ways in which datasets with a variety of characteristics can be processed in order to provide for new insights and understanding. Through treatment of the principles and fundamental requirements for ML/DM, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent data treatments, some of which lead to deep learning approaches. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.

Module provider

Computer Science

Module Leader

TAMADDONI NEZHAD Alireza (Computer Sci)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

Co-requisite: COMM054, Data Science Principles and Practices

Module content

Indicative content includes: Data Mining EDISON descriptors: KU1.01.02-KU1.01.05, KU1.01.08, KU1.03.01, KU1.03.04, KU1.03.05, KU1.03.06)

• Statistical paradigms (regression, time series, dimensionality, clusters)

• Probabilistic representations (causal networks, Bayesian analysis, Markov nets)

• Frequentist and Bayesian statistics

• Probabilistic reasoning

• Performance analysis

• Data mining and knowledge discovery

• Anomaly Detection

• Time series analysis

• Feature selection, Apriori algorithm Text Data Mining EDISON descriptors: KU1.04.02

• Data mining and text analytics Predictive Analytics EDISON descriptors, KU1.05.01

• Predictive modeling and analytics Machine Learning EDISON descriptors: KU1.02.01- KU1.02.05, KU1.02.08

• Machine Learning theory and algorithms

• Supervised Machine Learning

• Unsupervised Machine Learning

• Reinforced learning

• Classification methods

• Artificial Intelligence Appropriate software will be used for exploration of the above.

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.

Thus, the summative assessment for this module consists of:

• A coursework involving a report comprising the comparative evaluation of machine learning and data mining methods when applied and adapted, using common software tools to specified datasets, with documentation of key insights derived. This will evaluate LOs 1, 2, 3, and have a deadline in or near to week 11.

• An examination assessing both principles and practices of Machine Learning and Data Mining. This will evaluate LOs 1, 2, 3, 4. Formative assessment Students will be guided to work on weekly tasks through lab exercises, the solutions to which will provide for feedback on understanding and practice. Labs and feedback will then support the coursework. Individual feedback on the coursework will be given as soon as possible before the exam in order to feed forward to the exam.

Module aims

• This module aims to:
  elaborate, demonstrate, and apply a variety of machine learning and data mining approaches for the treatment of datasets of various kinds, and in ways which may sometimes be considered artificially intelligent.
  

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes.

The learning and teaching methods include: 11 teaching weeks with each week comprising:
2 hour lectures, to convey and discuss the key concepts and principles
2 hour lab sessions, to put key concepts and principles into practice

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COMM055

Other information

None.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2019/0 academic year.