Machine learning is the science of algorithms that improve their performance by learning from experience; most often in the form of data with or without labelled examples. Machine learning algorithms are used within a large number of application fields. Independently of the field, a developer of such algorithms need to have a systematic understanding of how a given assignment can be formulated as a machine learning problem. The aim of this course is to give you this systematic understanding. We will present a number of machine learning algorithms and statistical modelling algorithms. But above all, you will learn how the different algorithms are constructed, how they relate to one another and when they are applicable in theory and in practice.
FDD3434 Machine Learning, Advanced Course 7.5 credits
Information per course offering
Information for Autumn 2024 Start 28 Oct 2024 programme students
- Course location
KTH Campus
- Duration
- 28 Oct 2024 - 13 Jan 2025
- Periods
- P2 (7.5 hp)
- Pace of study
50%
- Application code
51578
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Part of programme
- No information inserted
Contact
Jens Lagergren (jensl@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FDD3434 (Autumn 2019–)Content and learning outcomes
Course contents
Intended learning outcomes
After the course, the students should be able to
*explain, derive and implement a number of models of supervised and unsupervised learning,
*analytically demonstrate how different models and algorithms relate to one another,
*explain strengths and weaknesses for different models and algorithms,
*choose appropriate model or strategy for a new machine learning task.
More specifically, regarding methodologies the student should be able to
*explain the EM-algorithm and identify problems where it is applicable,
*explain the terminology for Bayesian networks and topic models and apply these on realistic amounts of data,
*explain and derive boosting algorithms and design new boosting algorithms with different cost functions,
*explain and implement methods for learning of feature representations from various types of data.
Literature and preparations
Specific prerequisites
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- EXA1 - Examination, 7.5 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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- 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.