Course pages 2019–20
Probability and Computation
Lecture notes
- Lecture 1: Introduction (slides)
- Lecture 2: Concentration Inequalities (slides)
- Lecture 3: Concentration Inequalities II (slides)
- Lecture 4: Concentration Inequalities III (slides)
- Lecture 5: Conditional Expectation (slides)
- Lecture 6: Markov Chains (slides)
- Lecture 7: Random Walks and SAT (slides)
- Lecture 8: Convergence and Mixing Time (slides)
- Lecture 9: Linear algebra review and Markov chains (slides)
- Lecture 10: Mixing Time and Eigenvalues (slides)
- Lecture 11: Graph Clustering and Random Walks (slides)
- Lecture 12: Multiway clustering of graphs (slides). The material covered in this lecture is non-examinable.
- Lecture 13/14: Dimensionality Reduction (slides)
- Lecture 15: Online Learning using Expert Advice (slides). The material covered in this lecture is non-examinable.
- Monday 9th of March: Q&A Revision session, please send questions you want answered during this session to jas289
Additional Recommended Reading
Problem Sheets
- Problem Sheet 1 (Lectures 1-3)
- Problem Sheet 2 (Lectures 4-5)
- Problem Sheet 3 (Lectures 6-7)
- Problem Sheet 4 (Lectures 8-9)
- Problem Sheet 5 (Lectures 10-11)
- Problem Sheet 6 (Lectures 12+)