DD2421 Machine Learning 7.5 credits

Maskininlärning

Please note

The information on this page is based on a course syllabus that is not yet valid.

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
  • Grading scale

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

Course offerings

Autumn 18 SAP for Study Abroad Programme (SAP)

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    10026

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Schedule

    Schedule (new window)

  • Course responsible

    Atsuto Maki <atsuto@kth.se>

  • Teacher

    Atsuto Maki <atsuto@kth.se>

    Giampiero Salvi <giampi@kth.se>

    Örjan Ekeberg <ekeberg@kth.se>

  • Target group

    Only open for students within the SAP-programme.

Spring 19 mi19v for programme students

Spring 19 SAP for Study Abroad Programme (SAP)

  • Periods

    Spring 19 P3 (7.5 credits)

  • Application code

    20061

  • Start date

    15/01/2019

  • End date

    15/03/2019

  • Language of instruction

    English

  • Campus

    Stockholm

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Atsuto Maki <atsuto@kth.se>

    Giampiero Salvi <giampi@kth.se>

  • Teacher

    Atsuto Maki <atsuto@kth.se>

    Giampiero Salvi <giampi@kth.se>

    Örjan Ekeberg <ekeberg@kth.se>

  • Target group

    SAP students

  • Application

    Apply for this course at antagning.se through this application link.
    Please note that you need to log in at antagning.se to finalize your application.

Spring 20 miVT20 for programme students

Intended learning outcomes

The aim of the course is to give the students

  • basic knowledge of the most important 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

  • explain the principles, advantages, limitations such as overfitting 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 probability theory insofar they are relevant to machine learning The following topics in machine learning and computational intelligence are covered in detail
-nearest neighbour classifier
-decision trees
-bias and the trade-off of variance
-regression
-probabilistic methods
-Bayesian learning
-support vector machines
-artificial neural networks
-ensemble methods
-dimensionality reduction
-subspace methods.

Eligibility

For independent course students, 90 credits are required of which 45 credits in mathematics, informatics and/or SF1604 Linear Algebra as well as the courses SF1625 One variable calculus, SF1626 Multivariable analysis, SF1901 Mathematical Statistics, DD1337 Programming and DD1338 Algorithms and Data Structures or the equivalent.

Literature

Is announced on the course web page before start of the course.

Examination

  • LAB1 - Laboratory Work, 3.5, grading scale: P, F
  • TEN1 - Examination, 4.0, grading scale: A, B, C, D, E, FX, F

Offered by

EECS/Intelligent Systems

Contact

Atsuto Maki (atsuto@kth.se)

Examiner

Atsuto Maki <atsuto@kth.se>

Version

Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.