Subject: Machine learning 2 (17 - EK471)


Basic Information

CategoryTheoretical-methodological
Scientific or art field:Telecommunications and Signal Processing
InterdisciplinaryNo
ECTS6
Native organizations units

Course native organizational units not found!
Course specification

Course is active from 22.08.2017..


Precondition courses

Course idMandatoryMandatory
Machine learning 1YesNo
This course focuses on advanced machine learning topics with an emphasis on the theoretical foundations and the advanced implementation tools/practices. The topics go deep into specific supervised, unsupervised and semi-supervised algorithms addressing state-of-the-art machine learning techniques.
Students will be able to interpret and interrelate different advanced ML algorithms and approaches. Students will know how to approach the data, identify and select the most convenient ML approaches, regularization techniques, monitor training and adjust regularization parameters. Students will master the use of Python based toolboxes.
Neural Networks: Introduction, architectures and training procedures, evaluation and application. Ensemble learning: Bagging and boosting. Clustering - advanced algorithms, mixture models and expectation-maximization (EM) algorithm, ensemble clustering. Semi-supervised algorithms. Hidden Markov models. Probabilistic graphical models (inference, belief propagation, practical application).
Lectures, computer lab sessions (Matlab, Python), homework, consultations, active learning, project and research based learning, workshops.
AuthorsNameYearPublisherLanguage
Goodfellow, I., Bengio, Y., Courville, A.Deep Learning2017MIT Press, CambridgeEnglish
Kevin MurphyMachine Learning: A Probabilistic Perspective2012MIT PressEnglish
Bishop, C.M.Pattern Recognition and Machine Learning2006Springer, New YorkEnglish
Hastie, T., Tibshirani, R., Friedman, J.The Elements of Statistical Learning : Data Mining, Inference, and Prediction2009Springer, New YorkEnglish
Khanna, T.Foundations of Neural Networks1990Addison-Wesley, MassachusettsEnglish
Course activity Pre-examination ObligationsNumber of points
ProjectYesYes40.00
HomeworkYesYes5.00
HomeworkYesYes5.00
Written part of the exam - tasks and theoryNoYes50.00
Name and surnameForm of classes
Missing picture!

Sečujski Milan
Full Professor

Lectures
Missing picture!

Lončar-Turukalo Tatjana
Full Professor

Lectures
Missing picture!

Nosek Tijana
Asistent sa doktoratom

Computational classes
Missing picture!

Šobot Srđan
Assistant - Master

Computational classes