DD2447 Statistical Methods in Applied Computer Science 6.0 credits
Statistiska metoder i datalogin
This course summarizes statistical and probabilistic methods used in applied Computer Science.
Educational level
Second cycleAcademic level (A-D)
DSubject area
Computer Science and Engineering
Grade scale
A, B, C, D, E, FX, F
Course offerings
Autumn 12 TMAIM for programme students
Periods
Autumn 12 P2 (6.0 credits)
Application code
50198Start date
2012 week: 43End date
2013 week: 1Language of instruction
EnglishCampus
KTH CampusNumber of lectures
18 (preliminary)Number of exercises
Tutoring time
DaytimeForm of study
NormalNumber of places
No limitationSchedule
Schedule (new window)Course responsible
Jens Lagergren <jensl@kth.se>
Teacher
Jens Lagergren <jensl@kth.se>
Target group
Compulsary for TMAIM, Master Machine Learning, but available for all programs
Part of programme
- Master (Two Years), Computer Science, year 1, CSCF, Conditionally Elective
- Master (Two Years), Computer Science, year 1, CSCG, Conditionally Elective
- Master (Two Years), Computer Science, year 2, CSCF, Conditionally Elective
- Master (Two Years), Computer Science, year 2, CSCG, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIA, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIB, Mandatory
- Master (Two Years), Machine Learning, year 1, MAIC, Conditionally Elective
- Master (Two Years), Machine Learning, year 2, MAIA, Conditionally Elective
- Master (Two Years), Systems Biology, year 2, Optional
- Master (Two Years), Systems, Control and Robotics, year 1, Recommended
- Master (Two Years), Systems, Control and Robotics, year 2, Recommended
Autumn 13 statmet13 for programme students
Periods
Autumn 13 P2 (6.0 credits)
Application code
50186Start date
2013 week: 45End date
2014 week: 3Language of instruction
EnglishCampus
KTH CampusNumber of lectures
18 (preliminary)Number of exercises
14 (preliminary)Tutoring time
DaytimeForm of study
NormalNumber of places
No limitationSchedule
Schedule (new window)Course responsible
Jens Lagergren <jensl@kth.se>
Teacher
Jens Lagergren <jensl@kth.se>
Target group
Searchable för students at Master of Science in Engineering with at least 90 hp of which 50 hp from year 1. Searchable for students at Master of Science.
Part of programme
- Master (Two Years), Computer Science, year 1, CSCF, Conditionally Elective
- Master (Two Years), Computer Science, year 1, CSCG, Conditionally Elective
- Master (Two Years), Computer Science, year 2, CSCF, Conditionally Elective
- Master (Two Years), Computer Science, year 2, CSCG, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIA, Conditionally Elective
- Master (Two Years), Machine Learning, year 1, MAIB, Mandatory
- Master (Two Years), Machine Learning, year 1, MAIC, Conditionally Elective
- Master (Two Years), Machine Learning, year 2, MAIA, Conditionally Elective
- Master (Two Years), Machine Learning, year 2, MAIC, Conditionally Elective
- Master (Two Years), Mathematics, year 1, Optional
- Master (Two Years), Systems Biology, year 2, Optional
- Master (Two Years), Systems, Control and Robotics, year 1, Recommended
- Master (Two Years), Systems, Control and Robotics, year 2, Recommended
Learning outcomes
After successfully taking this course, you will be able to:
motivate the use of uncertainty management and statistical methodology in computer science applications, as well as the main methods in use,
account for algorithms used in the area and use the standard tools,
critically evaluate the applicability of these methods in new contexts, and design new applications of uncertainty management,
follow research and development in the area.
Course main content
Common statistical models and their use:
Hypothesis choice
Parametric inference
Non-parametric inference
Elements of regression
Clustering
Graphical statistical models
Prediction and retrodiction
Chapman-Kolmogoroff formulation
Elements of Vapnik/Chervonenki's learning theory
Evidence theory, estimation and combination of evidence.
Support Vector Machines and Kernel methods
Stochastic simulation, Markov Chain Monte Carlo.
Eligibility
Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B, or equivalent.
Prerequisites
Courses in mathematics (analysis), programming, computer science and statistics equivalent to obligatory courses on D- or F-programme.
Matlab or similar tool (Octave, R).
Literature
Lecture Notes, Scientific papers, Home Works.
Examination
- INL1 - Assignment, 6.0 credits, grade scale: A, B, C, D, E, FX, F
In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.
Requirements for final grade
Home Work (INL1; 6 university credits)
Based on learning contract individually worked out for each student.
Offered by
CSC/Computer Science
Contact
Jens Lagergren, e-post: jensl@kth.se
Examiner
Jens Lagergren <jensl@kth.se>
Add-on studies
Please discuss with the course leader.
Version
Course plan valid from:
Autumn 09.
Examination information valid from:
Autumn 07.