Academic Year 2020/21, Term 1
Department of Mathematics, The University of Manchester
Teaching staff:
Lecturer: Korbinian Strimmer
Office hour: Monday, 2pm. Please send
email
for an online appointment.
Overview and syllabus:
The MATH38161 module is an introductory course in Multivariate Statistics and Machine Learning for third year mathematics students. It is designed to run over the course of 11 weeks in six parts, each covering a particular aspect of multivariate statistics and machine learning:
- Multivariate random variables and estimation in large and small sample settings (W1 and W2)
- Transformations and dimension reduction (W3 and W4)
- Unsupervised learning/clustering (W5 and W6)
- Supervised learning/classification (W7 and W8)
- Measuring and modelling multivariate dependencies (W9)
- Nonlinear and nonparametric models (W10, W11)
The presentation of the material focuses on concepts and methods. In the worksheets the practical implementation and application in R of the methods is explored.
Flipped classroom:
The course is taught based on the principle of "flipped learning":
- First, guided by the weekly learning plan the material is studied asynchroneously from lecture notes and lecture videos and by attempting questions and problems on the corresponding worksheets.
- Subsequently, in synchroneous live review sessions the new material of each week is recapitulated and in live tutorials the tasks given the worksheets are discussed in an interactive fashion.
Course materials:
See also the MATH38161 UoM library reading listTo get started, download the lecture notes:
In addition, download the weekly learning plan and worksheets with solutions from Blackboard.
To help you understand the lecture notes view the associated lecture videos online:
- MATH38161 lecture videos: YouTube playlist / UoM video portal
Further information such as coursework instructions, previous exam papers, feedback to students etc. is available on Blackboard.
Dates and location:
Note that due to the new national restriction in the UK from 5 November 2020 the course is now taking place fully online, rather than in a blended fashion as initially intended (with some sessions from Tutorial group 1 taking place on campus).The synchronous live sessions take place at the following dates and locations:
| Session | Location | Day | Time | Semester 1 Week |
|---|---|---|---|---|
| Review lecture | Online - Blackboard | Monday | 13:00 | 1-11 |
| Tutorial group 1 | Online - Blackboard | Tuesday | 10:00 | 1-11 |
| Tutorial group 2 | Online - Zoom | Thursday | 10:00 | 1-11 |
Two online live sessions (review lecture, Tuesday tutorial) are delivered via Blackboard using Blackboard Collaborate Ultra. If you have trouble accessing the online course room on Blackboard please check that your browser is supported by Blackboard and that it is a recent version (recommended browser on desktop: Firefox ESR).
The third online live session (Thursday tutorial) is delivered via Zoom. The corresponding meeting ID and password is shown on Blackboard.
Recordings of the review lectures are available on Blackboard. The tutorial sessions are not recorded.
Assessment:
There is one in-semester assessment worth 20%. This is a small project requiring data analysis in R and writing of a corresponding statistical report, preferably in R Markdown.
The end-of-semester assessment is worth the remaining 80% and is concerned with theory and methods. It will be a 100% take home exam.
| Assessment | Date | Semester 1 Week |
|---|---|---|
| Project work (20%): | Announced: Monday 23 November 2020, 12 noon Submission: Monday 14 December 2020, 12 noon |
Work on project in weeks 8, 9 and 10 |
| Exam (80%): | Thursday 28 January 2021, 9am exam release, 12 noon submission | Last week of exam period |
The instructions for the in-semester project will be made available on Blackboard three weeks before the due deadline. The expected amount of time to complete the project is 10h.
Frequently asked questions and comments:
I hope you enjoy the course! If you have any questions, comments, or corrections please contact the lecturer. First check the MATH38161 FAQ whether your question has already been asked and answered before!
