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DD2431 Machine Learning 6.0 credits

Maskininlärning

Advanced course in computer science where the students learn to use methods from machine learning, computational intelligence and soft computing.

  • Educational level

    Second cycle

  • Academic level (A-D)

    C

  • Subject area

    Information Technology

  • Grade scale

    A, B, C, D, E, FX, F

Course offerings

Autumn 13 ml13 for programme students

Autumn 13 ml13 for programme students

  • Periods

    Autumn 13 P1 (6.0 credits)

  • Application code

    50475

  • Start date

    2013 week: 36

  • End date

    2013 week: 44

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Number of lectures

    24 (preliminary)

  • Number of exercises

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Schedule

    Schedule (new window)

  • Course responsible

    Örjan Ekeberg <ekeberg@kth.se>

  • Teacher

    Örjan Ekeberg <ekeberg@kth.se>

  • Target group

    Only for students at "Science without Borders"

Learning outcomes

The objective of this course is to give students

  • basic knowledge about the key algorithms and theory that form the foundation of machine learning and computational intelligence
  • a practical knowledge of machine learning algorithms and methods

so that they will be able to

  • understand the principles, advantages, limitations and possible applications of machine learning
  • identify and apply the appropriate machine learning technique to classification, pattern recognition, optimization and decision problems.

Course main content

The course is intended for both undergraduate and graduate students in computer science and related fields such as engineering and statistics. The course addresses the question how to enable computers to learn from past experiences. It introduces the field of machine learning describing a variety of learning paradigms, algorithms, theoretical results and applications.

It introduces basic concepts from statistics, artificial intelligence, information theory and control theory insofar they are relevant to machine learning. The following topics in machine learning and computational intelligence are covered in detail

-concept learning

-decision tree learning

-Bayesian learning

-artificial neural networks

-instance based learning

-computational learning theory

-evolutionary algorithms

-rule learning

-reinforcement learning.

Eligibility

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B, or equivalent.

Prerequisites

DD1321, DD1340 DD1343, DD1344, DD1346 or corresponding.

Literature

To be announced at least 4 weeks before course start at course web page. Previous year: T. Mitchell, Machine Learning, McGrawHill was used.

Examination

  • LAB1 - Laboratory Work, 3.0 credits, grade scale: P, F
  • TEN1 - Examination, 3.0 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

Laboratory assignments (LAB2, 3 credits).
Examination (TEN2, 3 credits).

Offered by

CSC/Computer Science

Contact

Örjan Ekeberg, e-post: ekeberg@kth.se

Examiner

Örjan Ekeberg <ekeberg@kth.se>

Add-on studies

Please discuss with the instructor.

Version

Course plan valid from: Autumn 09.
Examination information valid from: Autumn 08.