Course syllabus

# Maskininlärning

Machine Learning

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

## General Information

## Aim

## Learning outcomes

## Contents

## Examination details

## Admission

Admission requirements:## Reading list

## Contact and other information

Machine Learning

Valid for: 2018/19

Decided by: PLED F/Pi

Date of Decision: 2018-03-23

Elective for: BME4, D4-bg, D4-pv, E5, F4, F4-bs, F4-bg, F4-fm, F4-r, I4, Pi4-bg

Language of instruction: The course will be given in English

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.

Knowledge and understanding

For a passing grade the student must

- be able to account for the statistical principles used in machine learning
- be able to describe the scientific basis 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

- have demonstrated the abilitiy to develop learning techniques and learning systems for relevant technological problems
- have demonstrated the ability to identify, formulate, design, and implement learning components and applications.

Judgement and approach

For a passing grade the student must

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

- 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.

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)

Assessment: Compulsory assignments including computer work and written reports. Written examination. For those who do not get all the assignments approved during the course there will be a chance to hand in improved versions during the following semester.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

- FAMAF05 Mathematics - Systems and Transforms or FMAF10 Applied Mathematics - Linear systems

Required prior knowledge: FMAB20 Linear Algebra, FMAB30 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. FMSF45.

The number of participants is limited to: 80

Selection: Credits awarded in the courses FMSF45, FMSF20, FMSF10, FMSN40, FMAN20, FMAN60 and FMAN70.

- Bishop, C. M.: Pattern Recognition and Machine Learning. Springer, 2006, ISBN: 9780387310732.
- I. Goodfellow, Y. Bengio & A. Courville: Deep Learning. MIT press, 2016, ISBN: 978-0-262-03561-3. HTLM version available at http://www.deeplearningbook.org/, at least in November 2017.
- R. Sutton & A.G. Barto: Reinforcement Learning, An Introduction. MIT Press, 2017. Draft available at http://incompleteideas.net/sutton/book/the-book-2nd.html.

Teacher: Cristian Sminchisescu, cristian.sminchisescu@math.lth.se

Course coordinator: Anders Holst, studierektor@math.lth.se

Course administrator: Studerandeexpeditionen, expedition@math.lth.se

Course homepage: http://www.ctr.maths.lu.se/course/machinlearn/