Module Code | CS7CS4 |
Module Name | Machine Learning |
ECTS Weighting [1] | 5 ECTS |
Semester Taught | Semester 1 |
Module Coordinator/s | Professor Douglas Leith |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Understand what machine learning is and how it works;
- Understand and be able to apply machine learning algorithms such as linear regression, logistic regression, SVM, kNN and (deep) neural networks;
- Be able to effectively evaluate the performance of machine learning methods;
- Apply machine-learning frameworks (e.g. scikit-learn) to solve real-world problems;
- Write clear and effective reports to present machine learning results and analysis.
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;
- Experience of writing up machine learning analysis and results as a report.
Teaching and Learning Methods
Lectures and coursework.
Assessment Details
Assessment Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
Coursework | Weekly Assignments; Individual Project | LO1, LO2, LO3, LO4, L05 | 100% | Week 1 | TBC |
Reassessment Details
Assignment (100%).
Contact Hours and Indicative Student Workload
Contact Hours (scheduled hours per student over full module), broken down by: | 22 hours |
Lecture | 22 hours |
Laboratory | 0 hours |
Tutorial or seminar | 0 hours |
Other | 0 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 Hours | 116 hours |
Recommended Reading List
N/A
Module Pre-requisite
Prerequisite modules: N/A
Other/alternative non-module prerequisites: Python programming.
Module Co-requisites
N/A
Module Website
Links to classes for the first two weeks (for students who may wish to switch to this module):
Blackboard collaborate link.
Lecture slides are available at: www.scss.tcd.ie/doug.leith/CSU44061.