Statistical Learning
General information
Lecture: 
Monday, 10am  12pm, M5 
Lecturer: 

Assistance: 

QISPOS: 
The course in the course catalogue 
Prerequisites:  Probability Theory and Statistics; Probability Theory II may be beneficial 
Course syllabus: 
This course aims at an introduction of some basic aspects in statistical learning and data science. We plan to cover topics in stochastic approximation, pattern recognition, kernel density estimation, regression, supervised and unsupervised learning, and prediction. Knowledge of the fundamental concepts in Mathematical Statistics, especially about estimation and testing, are expected. 
Learnweb: 
The corresponding Learnweb course can be found here. The key is: StatisticalLearning 
Course assessment: 
There will be an oral exam/exam at the end of the course. Admission to the exam is conditional on obtaining at least 40% of the points of the problem sets. 
Exam:  tba 
Tutorial
Tutorial: 
Wednesday, 10am  12pm 
Problem sets: 
The problem sets can be found in the Learnweb course. 