Academic Year 2019/20, Term 2
School of Mathematics, The University of Manchester
Teaching staff:
Lecturer: Korbinian Strimmer (Office hour: Wednesday 10-11am, ATB 2.221)
Academic tutor: Christiana Charalambous
Student tutors: Ioanna Nikolopoulou and
Konstantin Siroki
Changes due to the Covid-19 pandemic:
All non-essential University sites have been closed on 17 March 2020. This course will now continue as online course.
- For Weeks 8 and earlier the official podcast is available from the UoM video portal.
- Lectures from Week 9 and later will be available in the "Video Lectures" folder on the course homepage on Blackboard.
- In addition to the lectures, I will also record videos explaining the solutions of the example sheets (for Week 8 and later).
- The handwritten slides from Week 8 and later are also available ona Blackboard in the "Handwritten Slides" folder.
- As a consequence of the pandemic there will be not be a final written exam. Instead a formative assessment will be available.
Frequently asked questions, feedback and email:
If you have any suggestions, comments, corrections (e.g. typos in notes) etc. you are most welcome to contact the lecturer directly by email. However, please remember that this is a very large class (approx. 250 students!) so please do check the MATH20802 FAQ to see whether your question has not already been asked and answered before!
Please also note that the size of the class does not allow for personal tuition via email. Therefore, to get feedback please attend the tutorial sessions and ask questions in person to your tutors. This will also benefit all students in your class! For further questions the lecturer is available at the end of each lecture and during the office hour.
Overview and syllabus:
For an outline of this course unit see MATH20802: Statistical Methods or download the course description as PDF.
Dates and location:
The course starts 27th January 2020 and runs until 5th May 2020. The tutorials start in week 2 on 4th February 2020.The course takes place at the following dates and locations:
Session | Time slot (location) | Term week |
---|---|---|
Lectures: | Monday, 3-4pm, and Tuesday, 3-4pm (Stopford TH1) | 1-11 |
Example classes: | Tuesday 1pm-2pm (Strimmer, Siroki), Wednesday 9am-10am (Strimmer, Siroki), Friday 9am-10am (Strimmer, Nikolopulou), Friday 10am-11am (Charalambous, Nikolopoulou) (all Alan Turing G209), Friday 12am-1pm (Charalambous, Nikolopoulou) (Univ. Place 1.218) |
2, 3, 4, 6, 8, 9, 11 |
Computer labs: | Groups and times as above for example classes (Alan Turing G105) | 5, 10 |
In-class test: | Groups and times as above for example classes (Alan Turing G105). The test will be an online assessment on Blackboard (40 minutes). |
7 |
Revision week: | Lectures only this week, no tutorials! | 12 |
In-class test and exam:
The in-class test is an online assessment on Blackboard and will take place in week 7 (worth 20%) in Alan Turing G105 during the usual example class / computer lab hours. The written exam (2 hours) is worth the remaining 80%.
Assessment | Date | Term week |
---|---|---|
In-class test (20%): | 10 March 2020 to 13 March 2020 (40 minutes) | 7 |
Written exam (80%): | TBA (2 hours) | Exam period |
For revision, the exam question the previous year (2018/19) are available on Blackboard.
Workload:
MATH20802 is a 10 credit module and correspondingly completion of this module requires about 100 hours study time. As a guideline the breakdown of the expected workload (101h) is as follows:
Type | Purpose | Study hours |
---|---|---|
Contact time | Total: 33h | |
Lectures (new material) | 11 x 2h = 22h | |
Lectures (revision) | 1 x 2h = 2h | |
Example classes | 7 x 1h = 7h | |
Software lab | 2 x 1h = 1h | |
Self-study | Total: 65h | |
Pre/post lectures work | 11 x 2h = 22h | |
Pre/post tutorials work: | 9 x 2h = 18h | |
Exam revision | 25h | |
Assessment | Total: 3h | |
Coursework | 1h | |
Exam: | 2h |
Course material:
Course material can be retrieved from Blackboard. This includes i) course notes, ii) the example sheets, iii) the instructions for the computer labs, and iv) previous exam questions.
See also the corresponding MATH20802 UoM library reading listIn addition, it is essential to study the material further using a text book. The following books are recommend to accompany this module:
- Held and Bove. 2014. Applied Statistical Inference: Likelihood and Bayes. Springer. Download PDF from Springer Link.
- Wood. 2015. Core Statistics. Cambridge University Press. Download PDF from Cambridge Core.
- Gelman et al. 2014. Bayesian data analysis (3rd edition). CRC Press. View online at UoM Ebook Central.
- Faraway, J. J. 2015. Linear Models with R (second edition). Chapman and Hall/CRC.
Further suggested (non-examinable) books are:
- Domingos. 2015. The Master Algorithm. Penguin. See book page on Wikipedia.
- Jaynes. 2003. Probability theory - the logic of science. Cambridge University Press. Download PDF from Cambridge Core.
- Diaconis and Skyrms. 2018. Ten great ideas about chance. Princeton University Press. See book page at PUP.
Lecture timetable and contents:
There will be 11 weeks of lectures and 1 week of revision. The course is divided into four parts. Part 1 (3.5 weeks, 7 lectures) covers likelihood estimation and inference. Part 2 (3.5 weeks, 7 lectures) introduces the essentials of Bayesian statistics. Part 3 (3.5 weeks, 7 lectures) focuses on the linear model. Part 4 (0.5 weeks, 1 lecture) is concerned with data protection and privacy.
Term week | Lecture (Date) | Content |
---|---|---|
1, 2, 3, 4 | 1-7 (27 Jan to 17 Feb) |
Maximum likelihood estimation and inference (7 lectures) |
4, 5, 6, 7 | 8-14 (24 Feb to 10 Mar) |
Bayesian statistics (7 lectures) |
8, 9, 10, 11 | 15-21 (16 Mar to 27 Apr) |
Linear regression (7 lectures) |
11 | 22 (28 Apr) |
Data protection and privacy (1 lecture) |
12 | 23-24 (4 May to 5 May) |
Revision lectures |
Corresponding lecture notes are available on Blackboard. The automated lecture capture system is active for this module so all lectures can be revisited online.
Tutorials timetable:
Term week | Tutorial (Date) | Topic |
---|---|---|
2 | 1 (4-7 Feb) |
KL divergence |
3 | 2 (11-14 Feb) |
Maximum likelihood |
4 | 3 (18-21 Feb) |
Maximum likelihood |
6 | 4 (3-6 Mar) |
Bayesian statistics |
8 | 5 (17-20 Mar) |
Bayesian statistics. |
9 | 6 (24-27 Mar) |
Linear regression |
11 | 7 (21 Apr-1 May) |
Linear regression |
The example class sheets are available on Blackboard.
Computer labs timetable and contents:
Term week | Lab (Date) | Topic |
---|---|---|
5 | 1 (25-28 Feb) |
Likelihood estimation and inference |
10 | 2 (21-24 Apr) |
Linear regression |
The material for each computer lab is available on Blackboard.
Note that the in-class exam in term week 7 (10-13 March) will also take place in the computer room!