MAS369 Machine Learning
|Semester 1, 2018/19||10 Credits|
|Lecturer:||Dr Frazer Jarvis||uses MOLE||Timetable||Reading List|
Machine learning lies at the interface between computer science and statistics. The aims of machine learning are to develop a set of tools for modelling and understanding complex data sets. Tools of statistical machine learning have become important in many fields, such as marketing, finance and business, as well as in science. The module focuses on the problem of training models to learn from existing data to classify new examples of data. Although other aspects of machine learning will be mentioned, the module focuses on the problem of classification; other topics in machine learning are covered by modules in Computer Science.
Prerequisites: MAS223 (Statistical Inference and Modelling)
No other modules have this module as a prerequisite.
- The main problems of data science and machine learning
- Data sets and data visualisation
- Dimensionality reduction - principal components analysis and introduction to other methods
- Supervised learning: the classification problem and discriminant analysis
- Regression and classification trees
- Ensemble methods and random forests; boosting
- Support vector machines
- Neural networks and deep learning
- Introduce students to the main problems in machine learning
- Introduce students to some of the techniques used for solving problems in data science
- Introduce students to neural networks and the main ideas behind "deep learning"
- Introduce students to the principal computer packages involved in machine learning
Lectures, lab sheets
20 lectures, no tutorials
Three projects of equal weight
|C||Everitt||An R and S-PLUS Companion to Multivariate Analysis|
|C||Hastie, Tibshirani and Friedman||The Elements of Statistical Learning|
|C||James, Witten, Hastie and Tibshirani||An Introduction to Statistial Learning|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop at Jessop West.
|Mon||14:00 - 14:50||lecture||Hicks Lecture Theatre C|
|Tue||16:00 - 16:50||lecture||Arts Tower Lecture Theatre 8|