FEO3274 Pattern Recognition, Machine Learning and Data Analysis 12.0 credits

Theoretical content: Bayes minimum risk criterion, maximum likelihood (ML),

maximum-a-posteriori (MAP), recognition for sequence of vectors, hidden Markov

model (HMM), graphical models, Gaussian process, expectation-maximization (EM),

approximate inference, variational Bayes, artificial neural network (ANN), back

propagation, vanishing gradient problem, deep learning, restricted Boltzman machines

(RBM), sparse representations, dictionary learning, convex optimization, greedy

methods, sparse kernel machines – relevance vector machine (RVM) and support

vector machine (SVM), graphical models, message passing, approximate message

passing, adaptive learning, online learning, learning over networks, doubly stochastic

networks, adaptation over networks.

Project content: Multimedia, gene sequence and financial data pre-processing, feature

extraction, and machine learning problems.

After the successful completion of course, the students should be able to

1. Identify and formulate recognition, learning and analysis problems given a

dataset.

2. Design systems and algorithms. Critically compare the algorithms in a tradeoff

between complexity and performance. Finally report the results.

3. Implement algorithms through convex optimization and Bayesian learning

approaches.

4. Contribute to the frontier research area.

Basic knowledge in linear algebra and probability theory

1. Pattern Recognition, Compendium by Arne Leijon and Gustav Henter.

2. Pattern Recognition and Machine Learning, by C.M. Bishop.

3. Deep learning methods and applications, by L. Deng and D, Yu.

4. Adaptation, learning and optimization over networks, by A.H. Sayed.

5. Sparse and redundant representations: from theory to applications in signal

and image processing, by M. Elad.

6. Advanced data analysis from an elementary point of view, by C.R. Shalizi.

7. Research paper handouts

If the course is discontinued, students may request to be examined during the following two academic years.

• EXA1 - Examination, 12.0 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.

1. Must need to pass the mid-term exam.

2. Must need to perform 3 given projects.

3. Must need to pass 2 master tests.

4. At-least 75% attendance in teaching classes.

5. Satisfactory performance in research project (preferably in own research area).

Expectation is to find innovative publishable research result and proper report.

6. Satisfactory performance in paper presentation.

Saikat Chatterjee

• 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.