Academic Year 2022/23, Semester 1
Department of Mathematics, The University of Manchester
Teaching staff:
Lecturer: Korbinian Strimmer
Office hour: Thursday, 12noon. Please send
email
for an appointment.
Graduate Teaching Assistants: Jacob Curran-Sebastian (W2, W7 - W12), Rajenki Das (W3), Xiaoxi Pang (W4 - W5)
Please read and follow the COVID-19 safety guidelines to help maintain a safe learning and study environment on campus!Overview and syllabus:
The MATH38161 module is an introductory course in Multivariate Statistics and Machine Learning for third year mathematics students. This module provides an introduction to classical multivariate statistics (multivariate random variables and distributions, transformations, dimension reduction methods such as PCA and CCA, multivariate dependence measures, multivariate mixture models) and also offers a primer to machine learning (unsupervised learning and clustering, supervised learning and classification, EM algorithm). At the end of this module there is a brief outlook to modern nonlinear and nonparametric machine learning methods (e.g. random forests, Gaussian processes, neural networks).
The modules 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 W7)
- Supervised learning/classification (W8 and W9)
- Measuring and modelling multivariate dependencies (W10)
- Nonlinear and nonparametric models (W11, W12)
The presentation of the material focuses on concepts and methods, not on theory. The practical implementation and application in R is another priority in this module. The example sheets contain both computational and programming problems as well as theoretical problems.
Teaching Format:
In the academic year 2022/23 this course is taught in traditional format with 2 lectures and 1 tutorial per week.
The main reference in this course are the MATH38161 lecture notes and you are expected to revisit the content of the lectures each week using these notes. The weekly learning plan available on Blackboard indicates which parts of the notes are relevant in each study week.
In the weekly tutorials the problems in worksheets are discussed. You are expected to attempt the problems within the week before the tutorials. Often this will involve some bits of programming and data analysis on the computer. You are welcome to work together in a group to solve the problems.
The teaching style returns to the traditional pre-pandemic "non-flipped" format. Hence, there is no need to watch any videos before coming to the lectures, and you also don't need to study the content before the lectures. However, it is essential to study the material of each week within the week before the next lectures and also solve the corresponding example sheet so that you don't fall behind and continue to be able to follow the subsequent lectures.
All lectures (but not the tutorials!) will be recorded automatically and corresponding podcasts will appear on the UoM video portal within 24 hours of the event.
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.
Further information such as coursework instructions, previous exam papers, feedback to students etc. is available on Blackboard.
Dates and location:
The lectures and tutorials take place at the following dates and locations:
| Session | Location | Day | Time | Semester 1 Week |
|---|---|---|---|---|
| Lecture 1 | Mansfield Cooper G19 | Wednesday | 11:00 | W1-W5, W7-W12 |
| Tutorial | Alan Turing G209 | Wednesday | 12:00 | W1-W5, W7-W12 |
| Lecture 2 | Zochonis TH B | Thursday | 11:00 | W1-W5, W7-W12 |
Assessment:
There is one in-semester take-home assessment worth 20%. This is 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. This exam will take place on campus in Manchester in January 2023.
| Assessment | Date | Semester 1 Week |
|---|---|---|
| Project work (20%): | Announced: Monday 14 November 2022, 12 noon Submission: Monday 28 November 2022, 12 noon |
Work on project in Semester W8 and W9 |
| Exam (80%): | Monday 16 January 2023, 9:45-11:45 | Exam period |
The instructions for the in-semester project will be made available on Blackboard on Monday 14 November, 12 noon. 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!
COVID-19 safety guidelines:
We have a collective responsibility to to reduce the risk of COVID-19 and other respiratory viruses to ourselves and others. Below are some measures that we can take to play our part - see also the University info page on "Coronavirus: Frequently asked questions".
- We strongly advise anyone who has COVID symptoms or tests positive to stay at home and to avoid contact with other people until your symptoms clear and you no longer test positive using a rapid lateral flow test. Please check the current NHS guidance for further details.
- We encourage everyone (if possible) to get vaccinated with an approved COVID-19 vaccine, and to keep vaccinations current.
- Wearing a face covering is highly recommended when there are a lot of respiratory viruses circulating and you are in close contact with other people in crowded and enclosed spaces (such as lecture theatres and teaching spaces). Face masks are provided for free at main building entrances.
- Help reduce crowding and keep buildings flowing safely by:
- arriving to the lecture theatre/classroom no earlier than 5 minutes before the start time, and
- departing the lecture theatre/classroom no later than 10 minutes before start of the next session.
