Instructor

Will Perkins

* w.f.perkins@bham.ac.uk*

Office: Watson 211

Office Hours: Tuesdays, 3:00pm - 4:00pm, Thursdays 10:00am-11:00am, or by appointment

Course Information
#### Main Topics of the Course:

- The statistical method
- Descriptive statistics
- Statistical inference
- Statistical significance
- Modern statistics: machine learning and algorithmic statistics
- Pitfalls of statistics:

- Probability theory
- Independence and conditioning
- Random variables
- Expectation and variance

- Limit theorems
- Convergence of random variables
- The Law of Large Numbers
- The Central Limit Theorem

Course materials

All course materials (lecture notes, problems sheets, review sheets, solution sheets, discussion forum) are on the University of Birmingham Canvas site.

Schedule

- Sep 27 Aims and methods of statistics
- Sep 29 Descriptive statistics
- Oct 4 Probabilistic models for data
- Oct 6 Outcomes, events, and probability
- Oct 11Inclusion, exclusion
Examples class

- Oct 13Independence and conditioning
- Oct 18Discrete random variables
- Oct 20 Political polling
- Oct 25 Continuous random variables
Examples class

- Oct 27Random vectors
- Nov 1 Class Test 1
- Nov 3Expectation
- Nov 8 Variance
Examples class

- Nov 10Covariance matrices
- Nov 15Second moment method
- Nov 17Law of Large Numbers
- Nov 22Convergence of random variables
Examples class

- Nov 24 Central Limit Theorem
- Nov 29Properties of the Normal distribution
- Dec 1Class Test 2
- Dec 6 Random walks
Examples class

- Dec 8 Statistics and prediction