FDD3431 Graduate Course in Machine Learning 6.0 credits
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Content and learning outcomes
The course is intended for 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
-decision tree learning
-artificial neural networks
-instance based learning
-computational learning theory
Intended 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.
Literature and preparations
To be announced at least 4 weeks before course start at course web page. Previous year: T. Mitchell, Machine Learning, McGrawHill was used.
Examination and completion
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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- 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.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web FDD3431
Main field of study
Please discuss with the instructor.