Timetable

Content is arranged by blocks (single week of teaching content).

There are 3 summative assessments (Assessment 1-2 + Portfolio), plus a formative (non-assessed) assessment and portfolio.

Semester 1

• 00 About
• Week 1: Block 01 Introduction to Data Science
  • Assessment 0 Set
  • Portfolio 0 Set
• Week 2: Block 02 Modern Regression and Cross Validation
• Week 3: Block 03 Loss Minimisation for Decisions, Boosting, Forests
  • Assessment 0 Due (Wednesday noon)
  • Assessment 1 Set
• Week 4: Block 04 Unsupervised learning: Latent Structures
  • Portfolio 0 Due (Wednesday noon)
  • Portfolio Set
• Week 5: Lecture contents from Block 05 Unsupervised learning: Outliers and Missingness 06 Decision Trees and Random Forests
• Week 6: Consolidation Week. No lectures.
• Week 8: Block 06 Perceptrons and Neural Networks
  • Assessment 1 Due (Wednesday noon)
  • Assessment 2 Set
• Week 9: Block 07 Topic Models and Bayesian Methods
• Week 10: Block 08 Algorithms for Data Science
• Week 11: Block 09 Parallel Algorithms
• Week 11: Block 10 Ethics and Privacy
  • Assessment 2 Due (Wednesday noon)
• Week 12: Assessment preparation week
  • Portfolio Due (Tuesday 4pm)