Statistical learning 1
level of course unit
Master's course
Learning outcomes of course unit
The following skills are developed in the course:
- Students are familiar with the functionality of basic algorithms in the field of data science.
- Students understand the statistical concepts and working methods behind the algorithms covered.
- Students are able to select suitable algorithms for given problems.
- Students are familiar with the data structures, runtime specifics and complexity classes required by the algorithms covered.
- Students can apply the algorithms in isolated problems.
prerequisites and co-requisites
1st semester: Students have previous knowledge of mathematics/statistics up to 8 ECTS and therefore know simple statistical measures as well as basic statistical test procedures (e.g. t-test). / 2nd semester: No prerequisites / 2nd semester: Module examination MLAL.A1 (Algorithmic 1)
course contents
The following content is discussed in the course:
- Statistical measures (point and interval estimators)
- Statistical test procedures
- Grouping algorithms (classification trees, agglomerative hierarchical clustering, etc.)
- Regression algorithms (regression trees, random forests, etc.)
- Associative algorithms
- Procedures for preprocessing data (e.g. principal component analysis)
recommended or required reading
PRIMARY LITERATURE:
- Murphy, K. P. (2012): Machine Learning: A Probabilistic Perspective (Ed. 1), MIT Press, Cambridge (ISBN: 978-0-262-01802-9)
- Bishop, C. (2006): Pattern Recognition and Machine Learning (Ed. 1), Springer-Verlag, New York (ISBN: 978-0-387-31073-2)
SECONDARY LITERATURE:
- James, G.; Witten, D; Hastie, T.; Tibshirani, R. (2013): An Introduction to Statistical Learning: with Applications in R (Ed. 1), Springer Science and Business Media, New York (ISBN: 978-1-461-471387)
- Steele, B.; Chandler, J.; Reddy, S. (2016): Algorithms for Data Science (Ed. 1), Springer, Berlin (ISBN: 978-3319457956)
assessment methods and criteria
Written exam
language of instruction
German
number of ECTS credits allocated
6
eLearning quota in percent
33
course-hours-per-week (chw)
3
planned learning activities and teaching methods
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Interactive workshop
semester/trimester when the course unit is delivered
1
name of lecturer(s)
Prof. (FH) Dr. Johannes Lüthi, Prof. (FH) Dr. Michael Kohlegger
recommended optional program components
none
course unit code
MLAL.1
type of course unit
integrated lecture
mode of delivery
Compulsory
work placement(s)
none