Statistical Learning 1
Niveau
Master's course
Learning outcomes of the courses/module
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.
- 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 for the course
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 content
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)
- 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 specialist literature
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)
- 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
English
Number of ECTS credits awarded
6
Share of e-learning in %
30
Semester hours per week
3.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Interactive workshop
- Lecture with discussion
- Processing of exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
1
Name of lecturer
Prof. (FH) Dr. Johannes Lüthi, Prof. (FH) Dr. Michael Kohlegger
Academic year
Key figure of the course/module
MLAL.1
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
none