DD2380 Artificial Intelligence 6.0 credits

Artificiell intelligens

Please note

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

The course gives a broad overview of the problems and methods studied in the field of artificial intelligence.

  • 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 ai18 for programme students

Autumn 18 SAP for Study Abroad Programme (SAP)

  • Periods

    Autumn 18 P1 (6.0 credits)

  • Application code

    10067

  • 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

  • Course responsible

    Jana Tumová <tumova@kth.se>

  • Teacher

    Iolanda Dos Santos Carvalho Leite <iolanda@kth.se>

    Jana Tumová <tumova@kth.se>

    Patric Jensfelt <patric@kth.se>

  • Target group

    Only open for students within the SAP-programme.

Spring 19 ai19vt for programme students

Autumn 19 aiHT19 for programme students

Intended learning outcomes

After completing this course the student should be able to

  1. recall and apply basic concepts in artificial intelligence
  2. solve problems from the AI domain
  3. demonstrate an insight into the risks of AI and its role in society
  4. present work

so that the student can 

  • make use of methods from artificial intelligence in the analysis, design and implementation of computer programs in academic as well as industrial applications

  • in an appropriate way present results and solutions.

Course main content

The following areas will be treated in the course: problem solving with search algorithms, heuristics and games, knowledge representation (logic), planning, representing uncertain knowledge and reasoning (Bayesian networks, HMM), decision and utility theory, natural language processing. 

Disposition

A series of lectures presents course material. Given the breadth of the course each lecture will not be able to go deep into a topic but rather focus on introducing the material. A couple of tutorials are added to this to provide a bit more in-depth coverage of some topics. The examination in the course consists of quizzes that test the basic concepts in the covered topics in the course, two lab assignments with programing in Java or C++ that go deeper into two areas and test the ability to solve problems in the AI domain and an optional project with research connection for higher grades. A criteria based grading system is used.

Eligibility

Single course students: 90 university credits including 45 university credits in Mathematics and/or Information Technology and the courses SF1604 Linear algebra, SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, DD1337 Programming and DD1338 Algorithms and Data Structures or equivalent. 

Recommended prerequisites

Programming skills in Java or C++ and skills in computer science corresponding to DD1337 Programming and DD1338

Algorithms and Data Structures

Furthermore SF1546 Numerical Methods Basic Course and SF1901 Probability Theory and Statistics or eqivalent.

Literature

Artificial Intelligence: A Modern Approach by Stuart J. Russell and Peter Norvig

Examination

  • LAB1 - Labs, 4.0, grading scale: P, F
  • TEN1 - Exam, 2.0, grading scale: P, 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

Pass the course components (LAB1; 4 university credits) and (TEN1; 2 university credits).

Offered by

EECS/Intelligent Systems

Contact

Jana Tumova, tumova@kth.se

Examiner

Iolanda Dos Santos Carvalho Leite <iolanda@kth.se>

Jana Tumová <tumova@kth.se>

Supplementary information

This course cannot be counted in the degree if the student has taken ID2209.

Add-on studies

DD2431 Machine Learning
DD2434 Machine Learning, advanced course
DD2424 Deep learning in data science
DD2432 Artificial Neural Networks and Other Learning Systems 
DD2423 Image Analysis and Computer Vision
DD2425 Robotics and Autonomous Systems
DD2429 Computational Photography
EL2320 Applied Estimation

Version

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