Course syllabus

Maskininlärning
Machine Learning

FMAN45, 7,5 credits, A (Second Cycle)

Valid for: 2016/17
Decided by: Education Board B
Date of Decision: 2016-03-29

General Information

Elective for: BME4, D4-bg, D4-pv, E4, F4, Pi4
Language of instruction: The course will be given in English

Aim

To give knowledge about the basic theory for Machine Learning -- construction of automatised systems that can learn/gather information from data, for example learn to recognize characters in a hand-written text.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

 

• have a good knowledge of the statistical principles used in machine learning
• have knowledge of the disciplinary foundation for the design and analysis of learning algorithms and systems
• demonstrate in-depth knowledge of methods and theories in the field of machine learning.

 

Competences and skills
For a passing grade the student must

 

• demonstrate abilities to develop learning techniques and systems based on relevant technological issues
• demonstrate the ability to identify, formulate, design, and implement learning components and applications.

 

Judgement and approach
For a passing grade the student must

demonstrate the ability to critically evaluate and compare different learning models and learning algorithms for different problem setups and quality characteristics.

Contents

 

• Training, testing, generalization, hypothesis spaces.
• Linear regression and classification.
• Kernel methods and support vector machines.
• Graphical models.
• Mixture models, Expectation Maximization.
• Variational and sampling methods.

 

Examination details

Grading scale: TH
Assessment: Compulsory assignments including computer work and written reports. Approved results on these are enough to pass the course. To get a higher grade, a written and an oral test are required.

Admission

Required prior knowledge: FMA420 Linear Algebra, FMA430 Calculus in Several Variables, Fourier analysis and theory of linear systems corresponding to FMAF05 Mathematics-Systems and Transforms, and one of the basic courses in Mathematical Statistics, e.g. FMS012.
The number of participants is limited to: No

Reading list

• Bishop, C. M.: Pattern Recognition and Machine Learning. Springer, 2006, ISBN: 9780387310732.

Contact and other information

Course coordinator: Cristian Sminchisescu, cristian.sminchisescu@math.lth.se
Director of studies: Anders Holst, studierektor@math.lth.se
Course homepage: http://www.ctr.maths.lu.se/course/machinlearn/