Module Overview

Machine Learning

Part-time / Level 9 / Online / 10 ECTS

This module covers the rationale, techniques and applications of Machine Learning. The aim of this module is to provide students with a comprehensive foundation and practical skills in machine learning including the underlying principles, the techniques used and applications of machine learning. Students will be required to use the techniques presented and to design and develop a machine learning system.

 

  • Introduction to Machine Learning: Classification, regression, generalisation, noise; feature representation and selection; model selection; supervised and unsupervised learning; parametric and non-parametric methods; applications of machine learning;
  • Information-Based Learning: Decision tree representation; basic decision tree learning algorithm ID3; choosing the best attribute, entropy and information gain; applications of decision trees
  • Probability-Based Learning: Bayes rule, prior probability, Naïve Bayes classifier
  • Similarity-Based Learning: k-Nearest Neighbour method; locally weighted regression; Case- Based Reasoning; lazy versus eager learning; applications of instance-based learning;
  • Error-Based Learning: Multiple Variable Linear Regression; Gradient Descent; Multinomial Logistic Regression; applications of MLR, Support Vector Machines, Neural Networks
  • Evaluation: Performance Measures, Experimental Methodology, Module Selection, Data Splitting Techniques
  • Dimensionality Reduction: Feature Selection, Principle Components Analysis;
  • Combining Multiple Learners: Ensemble learning, diversity in ensembles; base classifier generation methods, bagging, boosting; ensemble aggregation techniques;
  • Unsupervised Learning: Cluster analysis; applications of clustering; partitioning algorithms; k-means method; hierarchical method; cluster validity approaches.
  • Issues in Machine Learning: “curse of dimensionality”; concept drift; directions in machine learning research; overfitting

This module can be delivered either through standard delivery or blended delivery. In standard delivery this module is delivered through a series of lectures with associated practical assignments. In blended delivery this module is delivered through a series of live and recorded lectures with associated laboratory work and practical assignments. Both blended and standard delivery have the same overall number of teaching and self-directed learning hours.

Module Content & Assessment

Assessment Breakdown %
Formal Examination 50.00%
Other Assessment (s) 50.00%

Contact school.cs@tudublin.ie for further information.

EU students: €230

Non-EU students: Contact international.city@tudublin.ie for more details.