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.
Education cycle
Second cycleMain field of study
Computer Science and Engineering
Grading scale
A, B, C, D, E, FX, F
Course offerings
Autumn 18 statmet18 for programme students
-
Periods
Autumn 18 P2 (6.0 credits)
-
Application code
50977
Start date
29/10/2018
End date
14/01/2019
Language of instruction
English
Campus
KTH Campus
Tutoring time
Daytime
Form of study
Normal
-
Number of places
No limitation
Schedule
Planned timeslots
P2: F1, F2. more info
Course responsible
Jens Lagergren <jensl@kth.se>
Target group
Searchable for all students from year 3 and for students admitted to a master programme.
Part of programme
- Master's Programme, Computer Science, 120 credits, year 1, CSCS, Recommended
- Master's Programme, Computer Science, 120 credits, year 1, CSDA, Recommended
- Master's Programme, Computer Science, 120 credits, year 1, CSTC, Recommended
- Master's Programme, Computer Science, 120 credits, year 2, CSCS, Recommended
- Master's Programme, Computer Science, 120 credits, year 2, CSDA, Recommended
- Master's Programme, Computer Science, 120 credits, year 2, CSTC, Recommended
- Master's Programme, ICT Innovation, 120 credits, year 1, DASC, Optional
- Master's Programme, ICT Innovation, 120 credits, year 1, DASE, Optional
- Master's Programme, Industrial Engineering and Management, 120 credits, year 1, MAIG, Conditionally Elective
- Master's Programme, Machine Learning, 120 credits, year 1, Conditionally Elective
- Master's Programme, Machine Learning, 120 credits, year 2, Conditionally Elective
- Master's Programme, Mathematics, 120 credits, year 1, Optional
- Master's Programme, Mathematics, 120 credits, year 2, Optional
- Master's Programme, Systems, Control and Robotics, 120 credits, year 2, Recommended
Autumn 18 SAP for Study Abroad Programme (SAP)
-
Periods
Autumn 18 P2 (6.0 credits)
-
Application code
10070
Start date
29/10/2018
End date
14/01/2019
Language of instruction
English
Campus
KTH Campus
Tutoring time
Daytime
Form of study
Normal
-
Number of places
No limitation
Course responsible
Jens Lagergren <jensl@kth.se>
Target group
Only open for students within the SAP-programme.
Intended learning outcomes
After successfully taking this course, a student should be able to:
explain and justify several important machine learning methods,
account for several types of methods and algorithms used in the field, implement them using the book, and extend and modify them,
critically evaluate the methods’ applicability in new contexts and construct new applications,
follow research and development in the area.
Course main content
Basic statistical concepts and basic probability theory.
Generative models.
Bayesian inference.
Directed graphical models.
Undirected graphical models.
Exact inference for graphical models.
State space models.
Particle filters.
Monte Carlo estimation.
Sequential Monte Carlo.
Markov Chain Mote Carlo.
Clustering.
The Dirichlet process.
Eligibility
Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology.
Recommended 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
Machine Learning A Probabilistic Perspective av Kevin P. Murphy.
Kevin P. Murphy's "Machine Learning A Probabilistic Perspective"
Examination
- INL1 - Assignment, 6.0, grading scale: A, B, C, D, E, FX, F
Requirements for final grade
Assignments and a project (INL1; 6 university credits)
Offered by
EECS/ ?
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 syllabus valid from: Autumn 2016.
Examination information valid from: Spring 2019.