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Syllabus for

Academic year
TDA231 - Algorithms for machine learning and inference
 
Owner: TM
5,0 Credits (ECTS 7,5)
Grading: TH - Five, Four, Three, Not passed
Level: C
Department: 37 - COMPUTER SCIENCE AND ENGINEERING


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0101 Examination 5,0 c Grading: TH   5,0 c   Contact examiner,  Contact examiner

In programs

TM Teknisk matematik, Year 2 (elective)
TELTA ELECTRICAL ENGINEERING, Year 4 (elective)
BIMAS MSc PROGRAMME IN BIOINFORMATICS, Year 1 (elective)
EMMAS MSc PROGR IN ENGINEERING MATHEMATICS, Year 1 (elective)
TKBIA BIOENGINEERING, Year 4 (elective)
TITEA SOFTWARE ENGINEERING, Year 4 (elective)
TITEA SOFTWARE ENGINEERING, Year 3 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING - Algorithms, Year 4 (elective)

Examiner:

Bitr professor  Peter Damaschke



Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Basic courses in Algorithms and Mathematical Statistics.
Other courses which complement this course are AI, Applied Optimization and Information Theory.

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 intersting informa-tion 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.

Content

Introduction. Some basic concepts and definitions: learning, model, inference, utility. Different kinds of models and inference. Generalisation and bias.
Rule induction. Concept learning. Version space algorithms. Inductive bias. Decision trees: ID3 with variations. Overfitting. Learning sets of rules. CN2.
Neural networks. Basic principles. Hopfield nets. Feedforward nets. Backpropagation.
Instance based learning. Distance metrics. Nearest neighbour. Discriminant analysis. Case based reasoning. Cluster analysis.
Bayesian statistics. Conditional probability, Bayes theorem. Bayesian inference. Prior probability. Naive Bayesian classifier.
Probabilistic expert systems. Rule based expert systems. Graphical models/Markov graphs. Mathematical properties of graphical models.
Inference and learning in probabilistic expert systems. Inference in polytrees. Modifying the structure of general networks. Stochastic simulation. General learning difficulties. Algorithms for learning probabilistic networks.
Automatic design of algorithms through evolution. Short overview of evolutionary computation. Specification of algorithmic problems. Efficient incrememental search for programs. The ADATE system.
Theories of learning. Relation between Bayesian inference and learning. Minimum description length principle. Non-informative priors. Kolmogorov complexity. PAC learning. VC-dimension.
The course includes compulsory homework exercises, giving practical experience och the different kinds of algorithms.
See the course home page for the most up to date information.

Literature

Mitchell (1997): Machine Learning. Handouts.

Examination

Homework exercises plus a final report summarizing the course contents.


Page manager Published: Mon 28 Nov 2016.