Academic Year 2024/25, Semester 2
Department of Mathematics, The University of Manchester
Note:
This page is a placeholder (a copy of the page of last year). It will be updated in time before the start of semester 2 in January 2025.
Teaching staff:
Lecturer: Korbinian Strimmer
Office hour: Thursday, 11am. Please send
email
for an appointment.
Academic tutorial leads: C. Charalambous, Y. Han, S. Nadarajah, K. Strimmer
Overview and syllabus:
The MATH27720 Statistics 2 module is an introductory course in likelihood and Bayes inference for second year mathematics students. In this module you will learn about the foundations of statistical learning using likelihood and Bayesian approaches and also how these methods are underpinned by entropy.
The module is designed to run over the course of 11 weeks and has the following structure:
- Introduction and refresher (W1)
- Likelihood estimation and inference (W2–W5)
- Bayesian learning and inference (W6–W10)
- Revision (W11)
The presentation in this course is non-technical. The aim is to offer insights how diverse statistical approaches are linked and to demonstrate that statistics offers a concise and coherent theory of information rather than being an adhoc collection of “recipes” for data analysis (a common but wrong perception of statistics).
Teaching Format:
This course is taught in traditional format with 2 lectures per week and 1 biweekly tutorial.
The main reference in this course are the MATH27720 Statistics 2 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 biweekly tutorials the problems in worksheets are discussed. You are expected to attempt at least some of the problems before the tutorials. You are welcome to work together in a group to solve the problems.
The teaching style follows a traditional "non-flipped" format. Hence, you don't need to study the content before coming to the lectures. However, it is essential to revisit the material of each week before the next lectures 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 MATH27720 Statistics 2 UoM library reading listTo get started, download the lecture notes:
- MATH27720 Statistics 2 lecture notes (online)
- MATH27720 Statistics 2 lecture notes (PDF in A4 format for printing)
- MATH27720 Statistics 2 lecture notes (PDF in 6x9 inch format for use on tablets)
In addition, download the weekly learning plan and the worksheets with solutions from Blackboard. There you can also find instructions for the midterm test and previous exam papers.
Furthermore, the following supplementary notes will be useful:
These are also available in PDF format for A4 printing as well as for viewing on tablets.
Dates and location:
The lectures take place on campus at the following dates and locations:
| Session | Instructor | Location | Day | Time | Semester 2 Week |
|---|---|---|---|---|---|
| Lecture 1 | K. Strimmer | Engineering A Theatre A (2A.040) | Tuesday | 11:00 | W1-W6, W8-W11 |
| Lecture 2 | K. Strimmer | Engineering A Theatre A (2A.040) | Tuesday | 14:00 | W1-W11 |
There are no lectures in Week 12. Instead of the first lecture in Week 7 there is the midterm test. All lectures are recorded automatically and are available on the UoM video portal.
| Session | Instructor | Location | Day | Time | Semester 2 Week |
|---|---|---|---|---|---|
| Tutorial TUT2/1 | Y. Han | Roscoe 1.010 | Wednesday | 10:00 | W2, W4, W6 |
| Simon 2.61 | Wednesday | 10:00 | W8, W10 | ||
| Tutorial TUT2/2 | S. Nadarajah | Roscoe 3.3 | Wednesday | 12:00 | W2, W4, W6, W8, W10 |
| Tutorial TUT2/6 | K. Strimmer | Roscoe 1.010 | Thursday | 9:00 | W2, W4, W6 |
| Uni Place 5.206 | Thursday | 9:00 | W8, W10 | ||
| Tutorial TUT2/5 | K. Strimmer | Roscoe 1.010 | Thursday | 10:00 | W2, W4, W6 |
| Uni Place 5.206 | Thursday | 10:00 | W8, W10 | ||
| Tutorial TUT2/7 | C. Charalambous | Roscoe 3.3 | Thursday | 13:00 | W2, W4, W6, W8, W10 |
| Tutorial TUT2/3 | C. Charalambous | Zochonis Lecture Room E | Thursday | 15:00 | W2, W4, W6, W8, W10 |
| Tutorial TUT2/4 | S. Nadarajah | Roscoe 1.008 | Friday | 15:00 | W2, W4, W6, W8, W10 |
Assessment:
The in-semester assessment for Statistics 2 (midterm test) is worth 10% and is taken on campus in a computer lab. The end-of-semester assessment for Statistics 2 (written exam) is worth the 40% and is taken on campus.
Note that Semester 1 (Probability 2) and Semester 2 (Statistics 2) each contribute 50% of the total available marks for MATH27720. Half of the written exam will be about Statistics 2 and the other half about Probability 2.
| Assessment | Date | Semester 2 Week |
|---|---|---|
| Statistics 2 midterm test: | Tuesday 12 March 2024, 11:00-11:50, various computer labs, see below | W7 |
| Exam: | Wednesday 29 May 2024, 14:00-17:00, Sugden Sports Centre | Exam Period W3 |
The midterm test will take place in the following computer labs:
| Session | Instructor | Location | Day | Time | Semester 2 Week |
|---|---|---|---|---|---|
| Midterm test | Korbinian Strimmer | Engineering A_1A.011 CompCluster 1 | Tuesday | 11:00 | W7 |
| Midterm test | Robert Gaunt | Engineering A_1A.016 CompCluster 2 | Tuesday | 11:00 | W7 |
| Midterm test | Yang Han | Engineering A_1A.033 CompCluster 3 | Tuesday | 11:00 | W7 |
| Midterm test | Saralees Nadarajah | Engineering A_2A.021 CompCluster 4 | Tuesday | 11:00 | W7 |
| Midterm test | Christiana Charalambous | Alan Turing_G.105 | Tuesday | 11:00 | W7 |
| Midterm test | Peter Foster | Alan Turing_G.105A | Tuesday | 11:00 | W7 |
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 MATH27720 Statistics 2 FAQ whether your question has already been asked and answered before!
