Contemporary Statistics with Applications (22/23)

Uitgebreide vaknaam	Contemporary Statistics with Applications (tweejaarlijks 2022/2023)
Leerdoelen	The student is able to 1. apply a set of contemporary statistical methods to data. Those methods include: Linear methods for regression, Discriminant Analysis, Cubic Splines, Model Assessment and Selection Criteria, Additive Models, Boosting Approaches, Cross-Validation, Randomization Tests, the Expectation-Maximisation Algorithm and Hidden Markov Models. 2. derive elementary properties for the following methods: Linear methods for regression, Discriminant Analysis, Cubic Splines, Model Assessment and Selection Criteria, Additive Models, Boosting Approaches, Cross-Validation, Randomization Tests, the Expectation-Maximisation Algorithm and Hidden Markov Models.
Omschrijving	New measurement technologies in recent years have generated new data structures, which demand new forms of statistical analysis. High-dimensional data have become a feature of many quantitative sciences, as often it is very cheap to measure lots of things on the same statistical subject. Astronomists scan interstellar objects in many different wavelengths, microarrays measure the expression of thousands of genes on single sample, etc.. In such cases offer sparse inference techniques ways to overcome the large p, small n dilemma.
Uren per week
Onderwijsvorm	Hoorcollege (LC), Opdracht (ASM), Practisch werk (PRC), Werkcollege (T) (Weekly twice two-hour lectures and one two-hour practical. The practical will be held in a computer lab and involves programming in R.)
Toetsvorm	Opdracht (AST), Verslag (R) (Assessment takes place through three homework assignments and a written research project according to the formula: Final = 0.1 x max(HW1, R) + 0.1 x max(HW2, R) + 0.1 x max(HW3, R) + 0.7 x R, where HW1, HW2 and HW3 are the homework grades for 1st, 2nd, and 3rd homework and R = grade of the written project report.)
Vaksoort	master
Coördinator	prof. dr. M.A. Grzegorczyk
Docent(en)	prof. dr. M.A. Grzegorczyk
Verplichte literatuur	Titel Auteur ISBN Prijs The Elements of Statistical Learning Data Mining, Inference, and Prediction, 2nd ed., 2009, XXII, 746 p., Hardcover Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome 978-0-387-84857-0
Entreevoorwaarden	The course unit assumes prior knowledge acquired from the two course units Probability Theory and Statistics of the Mathematics BSc Programme.
Opmerkingen	This course was registered last year with course code WICSA-10
Opgenomen in	Opleiding Jaar Periode Type MSc Computing Science: Data Science and Systems Complexity  (Guided choice course units) - semester II a keuze MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester II a MSc Mathematics: Science, Business and Policy  (Science, Business and Policy: Statistics and Big Data) - semester II a keuzegroep MSc Mathematics: Statistics and Big Data  (MSc Mathematics: Statistics and Big Data) - semester II a keuzegroep Tweejaarlijkse vakken  (Even jaren) - semester II a -