*Schedule may be subject to change closer to the course

Monday 2022-09-12

E10:1307-E10:1309

09:00 - 10:00 Welcome (Olga, Eva)

10.00 - 10:45 Introductory lecture (Bengt)

11.00 - 12:00 Descriptive statistics [.presentation] (Olga)

12:00 - 13:00 lunch

13:00 - 13.30 Probability: introduction and discrete distributions (Eva)

13.30 - 14.30 Exercises: probability & discrete distributions

14.30 - 15.00 break

15:00 - 15:30 Probability: continuous distributions (Eva)

15:30 - 16:30 Exercises: continuous distributions

16.30 - 17.00 Daily challenge

Tuesday 2022-09-13

E10:1307-E10:1309

09:00 - 09:45 Group discussions: recap of the previous day

09:45-10:15 Statistical inference: Hypothesis tests using resampling incl. Multiple testing correction (Eva)

10:15-11:00 Exercises: hypothesis tests

11:00-11:30 Statistical inference: Parametric tests (Eva)

11:30 - 12:00 Exercises: parameteric tests

12:00 - 13:00 lunch

OBS! A9:117a

13:00 - 13:30 Statistical inference: Point and interval estimates (Eva)

13:30 - 14:30 Exercises: interval estimates

14:30 - 15:00 break

15:00-16:00 Non-parametric rank based tests [.presentation] (Olga)

16:30 - 17:00 Daily challenge

Wednesday 2022-09-14

E10:1307-E10:1309

09:00 - 09:45 Group discussions: recap of the previous day

09:45 - 11:00 Linear models: introduction [.presentation] (Olga)

11:00 - 12:00 Exercises: linear models introduction

12:00 - 13:00 lunch

13:00 - 14:00 Model coefficients & model diagnostics [.presentation] (Olga)

14:00 - 14:30 Exercises: model coefficients

14:30 - 15:00 break

14:30 - 15:30 Exercises: model diagnostics

15:30 - 16:30 Generalised Linear Models (Julie)

16:30 - 17:00 Daily challenge

Thursday 2022-09-15

E10:1307-E10:1309

09:00 - 09:45 Group discussions: recap of the previous day

09.45 - 10:45 Principal Component Analysis (Mun-Gwan) Extra PCA description

10.45 - 12:00 Exercises: PCA

12:00 - 13:00 lunch

13:00 - 13:30 Clustering: k-means and hierarchical clustering (Mun-Gwan) Presentation

13:30 - 14:30 Exercises: clustering

14:30 - 15:00 break

15:00 - 15:30 Introduction to supervised learning: classification and regression [.presentation] (Olga)

15:30 - 16:30 Exercises: introduction to supervised learning

16:30 - 17:00 Daily challenge

Friday 2022-09-16

E10:1307-E10:1309

09:00 - 09.45 Group discussions: recap of the previous day

09.45 - 10:30 Feature selection & model evaluation (Bengt)

10.30 - 12:00 Exercises: feature selection

12:00 - 13:00 lunch

13:00 - 14:00 Random Forest [.presentation] (Payam)

14:15 - 15:00 Exercises: Random Forest [.solutions]

15:00 - 17:00 Course wrap-up (Olga, Eva)

anytime after 15.00 Daily challenge

anytime after 15.00 Feedback survey