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
  • Assessment 0 Set
  • Portfolio 0 Set
• Week 2: Block 02 Regression and Statistical Testing
• Week 3: Block 03 Latent Structures, PCA, and Clustering
• Assessment 0 Due (Wednesday noon)
• Portfolio 0 Due (Wednesday noon)
• Assessment 1 Set
• Portfolio Set
• Week 4: Block 04 Non-parametrics and Missing Data
• Week 5: Lecture contents from Block 05 Supervised Learning and Ensembles and 06 Decision Trees and Random Forests
• Week 6: Consolidation Week. No lectures. You should work through the Workshop contents from Block 05 Supervised Learning and Ensembles and 06 Decision Trees and Random Forests, and catch up on Assessments.
• Week 7: Block 07 Perceptrons and Neural Networks
  • Assessment 1 Due (Wednesday noon)
  • Assessment 2 Set
• Week 8: Block 08 Topic Models and Bayesian Methods
• Week 9: Block 09 Algorithms for Data Science
• Week 10: Block 10 Parallel Algorithms
• Week 11: Block 11 Ethics and Privacy
  • Assessment 2 Due (Wednesday noon)
• Week 12: Assessment preparation week
  • Portfolio Due (Thursday noon)