F21ML Statistical Machine Learning

Course co-ordinator(s): Dr Ioannis Konstas (Edinburgh).

Aims:

In this course, students will develop:
• An understanding of the fundamental concepts and techniques used in data mining and machine learning.
• An understanding of the mathematics underpinning data mining and machine learning.
• A critical awareness of the appropriateness of different data mining and machine learning techniques and the relationships between them.
• Familiarity with common applications of data mining and machine learning techniques

Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: none.

Linked course(s): F21DL Data Mining and Machine Learning (Excluded).

Location: Edinburgh.

Semester: 1.

Syllabus:

Basic Concepts: classification, clustering, supervised and unsupervised learning.
Generative Models: probabilistic graphical models; cluster analysis (including k-means clustering, expectationmaximisation and mixture models); regression analysis.
Discriminative Learning: Instance-based learning and decision tree learning; artificial neural networks (perceptron, multilayer perceptron, back-propagation, deep learning architectures); maximum entropy models; support vector machines; ensemble methods (such as bagging and boosting).

Learning Outcomes: Subject Mastery

• Introduction to the fundamental concepts in data mining and machine learning.
• Detailed and integrated knowledge and understanding of the mathematics underpinning the data mining and machine learning methodologies.
Critical awareness of the appropriateness and performance of the different techniques, as well as the relationships between them.

Learning Outcomes: Personal Abilities

• Rational problem identification and definition.
• Critical analysis and substantial autonomy in selecting the correct solution to a given problem.
• Manage time, work to deadlines, and prioritise workloads.
• Demonstrate the ability to learn independently, as well as deal with complex issues under time constraints.
• Use appropriate computer software to process data.
• Present results in a way that demonstrates a deep understanding of the technical and broader issues of data mining and machine learning.

Assessment Methods:

Assessment: Examination: (weighting – 60%) Coursework: (weighting – 40%)
Re-assessment: Examination: (weighting – 100%)

SCQF Level: 11.

Credits: 15.