Machine Learning in Signal Processing

Course: Machine Learning in Signal Processing

Code: 3ФЕИТ05010

ECTS points: 6 ECTS

Number of classes per week: 3+0+0+3

Lecturer: Asst. Prof. Dr. Tomislav Kartalov

Course Goals (acquired competencies): The students that finish this course, should be able to: - Decompose, analyze, classify, detect and consolidate signals - Develop appropriate models for measured signals/data - Choose the appropriate tool for feature extraction - Assess / Evaluate the advantages and limitations of different signal processing tools for a given problem - Derive the supervised and unsupervised learning techniques studied in class - Choose an appropriate learning algorithm for a given problem - Develop basic supervised and unsupervised learning models - Assess / Evaluate the advantages and limitations of different machine learning algorithms.

Course Syllabus: Representing Sounds and Images. Introduction to Linear Algebra. Signal Representations - Component Analysis. Eigen representations: Eigenfaces. Boosting. PCA. ICA. NMF. Sparse NMF. Clustering. SVM. Mixture Models and EM. Linear Regression. Logistic Regression. Markov and Hidden Markov Models. Neural Networks. Deep Learning. Convolutional Networks.

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