STU33009: Statistical Methods for Computer Science 2020-21

Lectures: Weds 3-5pm Online
Tutorials: Weds 1-2pm Online
Lecturer: Doug Leith

The module is a first course in probability and statistics that only assumes basic mathematical (algebra, sets) and programming (we'll use matlab) knowledge. The aim is to take students to the point where they have the foundation needed for understanding machine learning techniques (and so for a follow on module on machine learning or to read an introductory book), performance analysis of computer systems etc.

The module is divided into four parts (each roughly three weeks long) and the topics covered in each part are summarised here.

Assessment is 60% final exam/assignment, 30% mid-term exam/assignment (held directly after reading week) and 10% marked weekly questions.

 Week 1 Counting. Slides. Videos: Counting Permutations. Slides. Videos: Permutations, Combinations Matlab Lab handout. Test questions. Solutions. More test questions. Solution checker. Solutions. Weekly Questions. Solution checker Week 2 Axioms of Probability. Slides. Video: Sample space, Axioms, Equally likely outcomes Algebra revision. Slides. Video: Algebra Conditional Probability and Bayes Theorem. Slides. Video: Conditional prob, Marginalisation Bayes rule Test questions. Solutions. More test questions. Solution checker. Solutions. Weekly Questions. Solution checker Week 3 Independence. Slides. Videos: Independence, Examples Review of material to date (for study in own time). Slides. Random Variables. Slides. Test questions. Solutions. More test questions. Solution checker. Solutions. Weekly Questions. Solution checker Week 4 Random Variables. Slides. Videos: Random variables, PMF and CDF, Bernoulli and Binomial RVs, Simulation Mean and Variance. Slides. Videos: Expected Value, Expected Value (cont), Variance Test questions. Solutions. More test questions. Solution checker. Solutions. Weekly Questions. Solution checker Week 5 Correlation and Conditional Expectation. Slides. Videos: Pairs of RVs, Covariance and correlation, Conditional expectation Review (for study in own time). Slides. Inequalities. Slides. Videos: Markov's inequality, Chebyshev's inequality Test questions. Solutions. More test questions. Solution checker. Solutions. Weekly Questions. Solution checker Week 6 Sample mean, Weak law of large numbers and CLT. Slides. Videos: Sample mean, Weak law and Central Limit Theorem (CLT) Confidence Intervals. Slides. Videos: Confidence intervals, Examples, Bootstrapping Test questions. Solutions. More test questions. Solution checker. Solutions. Week 7 Week 8 No Lecture Weekly Questions. Week 9 Logistic Regression. Slides. Videos: Logistic regression 1, Logistic regression 2, Logistic regression 3, Logistic regression 4 Test questions. Solutions. Weekly Questions. Week 10 Into to Linear Regression. Slides. Video: Into to Linear Regression Continuous random variables. Slides. Videos: Continuous RVs 1, Continuous RVs 2, Continuous RVs 3, Continuous RVs 4 Test questions. Solutions. More test questions. Solution checker. Solutions. Even more test questions. Solution checker. Solutions. Weekly Questions. Week 11 Probabilistic Interpretation Of Linear Regression. Slides. Videos: Linear Regression, Linear Regression (cont) Test questions. Solutions. More test questions. Solution checker. Solutions. Week 12