CSU44061 – Machine Learning

Module CodeCSU44061
Module Name Machine Learning
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 1
Module Coordinator/s  Professor Douglas Leith

Module Learning Outcomes

On successful completion of this module, students will be able to:

  1. Understand what machine learning is and how it works;
  2. Understand and be able to apply machine learning algorithms such as linear regression, logistic regression, SVM, kNN and (deep) neural networks;
  3. Be able to effecively evaluate the performance of machine learning methods;
  4. Apply machine-learning frameworks (e.g. scikit-learn) to solve real-world problems.

Module Content

  • Prediction using machine learning;
  • Choice of features, including for text, images, time series;
  • Model selection (e.g. linear, kernel, neural net);
  • Learning as empirical risk minimisation;
  • Common machine learning techniques (linear regression, logistic regression, SVMs, kernel trick, neural nets, convolutional neural nets, kNN, k-Means);
  • Evaluating machine learning methods (cross-validation, bootstrapping, ROC, use of a baseline);
  • Practical experience of applying machine learning methods to real data.

Teaching and Learning Methods

Lectures and coursework.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
ExaminationAssignment Applying ML MethodsLO1, LO2, LO3,
LO4, L05
CourseworkWeekly Assignments, Individual ProjectLO1, LO2, LO3,
LO4, L05

Reassessment Details

Assignment (100%).

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by: 22 hours
Lecture22 hours
Laboratory0 hours
Tutorial or seminar0 hours
Other0 hours
Independent Study (outside scheduled contact hours), broken down by:94 hours
Preparation for classes and review of material (including preparation for examination, if applicable)44 hours
Completion of assessments (including examination, if applicable)50 hours
Total Hours116 hours

Recommended Reading List


Module Pre-requisites

Prerequisite modules: STU33009

Other/alternative non-module prerequisites: Python programming. Basic knowledge of probability and statistics at a level similar to that provided by module STU33009.

Module Co-requisites


Module Website


Links to classes for the first two weeks (for students who may wish to switch to this module):

Blackboard collaborate link.

The lecture slides are also available at www.scss.tcd.ie/doug.leith/CSU44061.