Unit information: Advanced Linear Modelling and Classification in 2022/23

Unit name	Advanced Linear Modelling and Classification
Unit code	MATH20016
Credit points	20
Level of study	I/5
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Professor. Andrieu
Open unit status	Not open
Units you must take before you take this one (pre-requisite units)	MATH10013 Probability and Statistics, MATH10016 Matrix Algebra and Linear Models
Units you must take alongside this one (co-requisite units)	MATH20800 Statistics
Units you may not take alongside this one	None
School/department	School of Mathematics
Faculty	Faculty of Science

Unit Information

Lecturers: Christophe Andrieu and Mathieu Gerber

The unit aims to provide students with an overview of modern regression and classification methods and skills to use relevant R packages to analyse simple datasets.

Topics will include:

• An overview of regression and classification
• Generalised linear models, maximum likelihood estimation and random effects
• Classification: KNN, linear discriminant analysis and Logistic regression,
• Model diagnosis,
• Model assessment and selection (Bias-variance trade-off, overfitting, subset selection, resampling methods, regularisation.)
• Basis expansions, local regression, generalised additive models
• Introduction to kernel-based methods
• Associated computing (R functions lm, glm, lda, knn, loess, R packages mgcv, glmnet, splines).

Teaching will be delivered via a mix of lectures and computer labs.

Your learning on this unit

By the end of the course, students should be able to:

1. Identify appropriate regression or classification methods when presented with applied problems requiring their use.
2. Use and critically assess a range of modern regression and classification methods.
3. Perform model diagnosis and model selection.
4. Demonstrate understanding of how the methods covered in the unit work.
5. Demonstrate the ability to use the methods in R, and to interpret the results appropriately.
6. Demonstrate an understanding of the relationship between the methods and models taught, and of their relative strengths and weaknesses

How you will learn

Lectures, computer labs, problem sheets and computer practicals

How you will be assessed

Written exam (50%) and computer practicals (50%)

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MATH20016).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.