FI:MV013 Statistics - Course Information

MV013 Statistics for Computer Science

Extent and Intensity

2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).

Teacher(s)

doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Mojmír Vinkler (seminar tutor)

Guaranteed by

doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science

Timetable

Mon 8:00–9:50 A319

• Timetable of Seminar Groups:

MV013/T01: Tue 20. 9. to Thu 22. 12. Tue 8:00–9:35 110, M. Vinkler, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MV013/01: Mon 10:00–11:50 B117, M. Vinkler
MV013/02: Mon 12:00–13:50 B117, M. Vinkler

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (eng.) (programme FI, N-IN)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Information Systems (programme FI, N-IN)
• Informatics (eng.) (programme FI, D-IN4)
• Informatics (programme FI, D-IN4)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems and Technologies (eng.) (programme FI, D-IN4)
• Computer Systems and Technologies (programme FI, D-IN4)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Embedded Systems (programme FI, N-IN)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

Syllabus

• Why computer scientists should study statistics?
• Computer science related problems with analysed data
• Why the thought study based on data is useful?
• Data types
• Sampling
• Parametric probabilistic and statistical models
• Likelihood principle and parameter estimation using numerical methods
• Descriptive statistics (tables, listings, figures)
• From description to statistical inference
• Hypothesis testing and parameters of a model
• Goodness-of-fit tests
• Testing hypotheses about one-sample
• Testing hypotheses about two-samples
• Testing hypotheses about more than to sample problems
• Interpretation of statistical findings

Literature

• CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info

Language of instruction

English

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (Autumn 2016, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2016/MV013