FDD3342 Knowledge Discovery and Data Mining 6.0 credits

KDD philosophy
Bayes rule and its interpretation as inference tool
Learnability, VC-dimension 
Statistical Techniques: MV analysis, SVD technique, etc.
Classification and clustering
Bayesian networks and graphical models
Prediction and sequence mining
Markov Chain Monte Carlo methods
Support vector and kernel methods

After passing this course, you will:

• know the fundamental approaches to knowledge discovery and data mining, the main theoretical foundations, as well as its code of practice,
• know about several tools in the area and be able to use at least one,
• be able to follow research and development in the area,
• be able to assess the applicability of the technology for a particular scientific problem area, and develop the scientific methods used.

Student or doctoral student with first courses passed in programming and statistics.

If the course is discontinued, students may request to be examined during the following two academic years.

• EXA1 - Examination, 6.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Examination is individual, and can consist of presentations of texts, presentation in class, homeworks and/or a small project. A list of papers you read for the course, preferrably with comments, and proposals for course improvement should be turned in.

Jens Lagergren

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

The course can be taken as a reading course, but is also co-lectured with DD2447 Statistical Methods in Computer Science.