|Module Name||Machine Learning|
|ECTS Weighting ||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 effecively evaluate the performance of machine learning methods;
- Apply machine-learning frameworks (e.g. scikit-learn) to solve real-world problems.
- 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 Component||Brief Description||Learning Outcomes Addressed||% of Total||Week Set||Week Due|
|Examination||Assignment Applying ML Methods||LO1, LO2, LO3,|
|Coursework||Weekly Assignments, Individual Project||LO1, LO2, LO3,|
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||22 hours|
|Tutorial or seminar||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
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.
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.