F21ML - Statistical Machine Learning
Course leader(s):
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.
Syllabus
1. ML Fundamentals
Boundary Classifiers
Linear Models
Non-Linear Models
Probabilistic Modelling
Unsupervised Learning (1.1 Intro to ML, 1.2 Decision Trees, 1.3 Basic Concepts, 1.4 k-NN, 1.5 Perception, 1.6 Features and Evaluation, 1.7 Linear Model, 1.8 Unsupervised Learning, 1.9 k-means, 1.10 Neural Networks, 1.11 Probabilistic Modeling, 1.12 Logistic Regression, 1.13 Deep Learning, 1.14 Advanced ML topics and Revision)
Learning outcomes
By the end of the course, students should be able to do the following:
-
demonstrate an understanding of the basic concepts of machine learning, such as classification, clustering, regression, supervised and unsupervised learning, and be able to analyse data using these concepts in an appropriate computer package.
- demonstrate an understanding of neural networks and deep learning, and be able to analyse data using these machine learning skills.
- demonstrate a critical understanding of generative and discriminative models and their application to statistical machine learning tasks.
- use appropriate machine learning packages to read and analyse data.
- communicate the results and conclusions of a machine learning analysis in a manner that is clear, concise and appropriate for the audience.
Further details
Curriculum explorer: Click here
SCQF Level: 11
Credits: 15