Syllabus for |
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| TDA231 - Algorithms for machine learning and inference |
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| Syllabus adopted 2015-02-10 by Head of Programme (or corresponding) |
| Owner: MPALG |
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7,5 Credits |
| Grading: TH - Five, Four, Three, Not passed |
| Education cycle: Second-cycle |
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Major subject: Computer Science and Engineering, Information Technology |
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Department: 37 - COMPUTER SCIENCE AND ENGINEERING
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The course is full
Teaching language: English
Open for exchange students
Block schedule:
D
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0101 |
Project |
7,5c |
Grading: TH |
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7,5c
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In programs
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (elective)
Examiner:
Professor
Devdatt Dubhashi
Bitr professor
Peter Damaschke
Eligibility:
In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement:
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.
Course specific prerequisites
Programming in high level languages (MATLAB experience desirable but not absolutely required)
- elementary linear algebra and probability
- basic algorithms course
Aim
This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model.
Algorithms of this kind are commonly used in for example classification tasks (character recognition, or to predict if a new customer is creditworthy etc.) and in expert systems (for example for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases.
The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature.
Learning outcomes (after completion of the course the student should be able to)
- apply sound mathematical foundations to the inference of hypotheses from empirical data and models on scientific grounds;
- explain a representative set of available Machine Learning approaches;
- evaluate the methods qualitatively and quantitatively, and to recognize both their strengths and limitations.
Content
- Supervised Learning: Bayes classifier, Fisher discriminant, Perceptron, Support vector machines, Regression, Boosting;
- Unsupervised Learning: Clustering algorithms, EM algorithm, Mixture models, Kernel methods;
- Graphical Models: Hidden Markov models, Belief propagation.
Organisation
Lectures and homework assignments.
Literature
See course homepage.
Examination
Assignments and exam, with equal weightage.