Semester page for IN-STK5000 - Autumn 2019

• Schedule
• Examination: Time and place

Assignments

• Introductory assignment
• Reproducibility assignment

• Project: Credit risk
• Project: Medical treatment

Lecture videos

• Lecture 1, Part 1
• Lecture 1, Part 2
• Lecture 1, Part 3
• Lecture 1, Part 4
• Lecture 2, Part 1: Bayesian Inference
• Lecture 2, Part 2: Bayesian Inference Exercise
• Lecture 3: Decision Problems
• Lecture 4.1: Databases, Anonymity, Privacy, Randomised Response
• Lecture 4.2: Randomised Response, Differential Privacy
• Lecture 4.3: Example for Local and Central Differential Privacy, Laplace Mechanism
• Lecture 4.4: Laplace Mechanism, Central and Local differential privacy. Exponential mechanism. Reusable Holdout.
• Lecture 5.1: Fairness, graphical models
• Lecture 5.2: Inference, prediction, hierarchical models
• Lecture 5.3: Bayesian model inference, confidence intervals, testing independence

• Lecture 5.4: Testing conditional independence
• Lecture 5, Explainer: Code for conditional Independence
• Project 1 QA
• Lecture 6.1: Recommendation systems
• Lecture 6.2: Latent variable models
• Lecture 7.1: Causality
• Lecture 7.2: Causality
• Lecture 7.3: Causality
• Lecture 7.4: Causality
• Lecture 8.1: Experiment design
• Lecture 8.2: Experiment design and MDPs
• Lecture 8.3: One-shot experiment design in bandits
• Lecture 8.4: Reinforcement learning

Course literature

• Course notes
• Differential privacy

• The ethical algorithm
• Decision making under uncertainty and reinforcement learning