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Module Descriptor School of Computer Science and Statistics

Module CodeCS7CS4
Module NameMachine Learning
Module Short Title
Semester TaughtMichaelmas Term
Contact Hours

2 lecture hours per week, and a few additional lab sessions (around four in total).

Module PersonnelAssistant Professor Joeran Beel & Professor Douglas Leith
Learning Outcomes

Students who complete this module should be able to:

  • CS4LO1 Decide when machine learning is an appropriate method to solve a problem
  • CS4LO2 Understand how machine learning works.
  • CS4LO3 Apply machine-learning frameworks to solve real-world problems, and adjust and extend existing algorithms when necessary.
Learning Aims

The principal aim of this module is to provide students with a working understanding of machine-learning techniques and their application to solve real-world problems.

Module Content
  • Machine Learning Basics (Application Areas, Challenges, Alternatives to Machine Learning)
  • Machine Learning in Action (Datasets, Frameworks, Evaluation)
  • Cross-validation and confidence intervals
  • Overfitting/underfitting (bias-variance trade-off)
  • Machine Learning Algorithms
    • Linear Regression
    • Logistic Regression
    • Support Vector Machines
    • Kernel Methods
    • k-Means Clustering and Mixture Models for Unsupervised Learning
    • Neural Networks
    • Deep Learning Algorithms
  • Use of gradient descent, and extensions for improved scalability (stochastic gradient descent etc)
  • Probabilistic interpretations of ML algorithms.  Maximum Likelihood and MAP estimators.
  • Recommender systems
Recommended Reading List
Module Prerequisites

A first course in probability and a basic knowledge of linear algebra

Assessment Details

Coursework: 40%

Exam: 60%

Assessment in the Supplemental session will be based on 100% exam.

Module Website
Academic Year of Data2018/19