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Module

CSC3832 : Predictive Analytics and Machine Learning (Inactive)

  • Inactive for Year: 2020/21
  • Module Leader(s): Dr Paolo Missier
  • Lecturer: Dr Stephen McGough
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

This module aims to provide a foundation in the field of Pattern Recognition and an expertise in Machine Learning techniques as a toolkit for automatically analysing (large amounts of) data – be it static data, such as images, or dynamic data, such as time series and sensor data.

Outline Of Syllabus

•       Fundamental data representations for machine learning
•       Traditional machine learning and deep learning, basics
•       Traditional supervised learning methods: linear regression, logistic regression, naïve bayes, decision tree, random forest, support vector machines, k-nearest neighbours classifier
•       Clustering methods, k-means, expectation maximisation
•       Deep learning: multilayer perceptron, convolutional neural network, recurrent neural network, autoencoder
•       Principle component analysis, linear discriminant analysis

Teaching Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Assessment Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Reading Lists

Timetable