| title | Coursebook |
|---|---|
| layout | coursebook |
Content is arranged by blocks (single week of teaching content). You will not miss any material moving through this sequentially as the reference information is cited throughout.
We will spend the first half of each session with an introduction to the method, and the second half with students presenting and discussing portfolio work in small groups.
- 00 About
- Block 01 Introduction to Data Science
- Block 02 Modern Regression and Cross Validation
- Block 03 Loss Minimisation for Decisions, Boosting, Forests
- Block 04 Unsupervised learning - Latent Structures
- Block 05 Unsupervised learning - Anomalies, p-values and Missingness
- Block 06 Perceptrons and Neural Networks
- Block 07 Topic Models and Bayesian Methods
- Block 08 Algorithms for Data Science
- Block 09 Parallel Algorithms
- Block 10 Ethics and Privacy
- Assessments
- Timetable
- Appendix 1: Preparation
- Appendix 2: Replicability
- Appendix 3: GitHub
- Appendix 4: How to Read a Paper
- Programme Catalogue Course Handbook
- Block 11 Parallel Infrastructure and Spark is unassessed but provided for reference.