Machine Learning in Signal Processing
Course: Machine Learning in Signal Processing
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.
|C.M. Bishop||Pattern Recognition and Machine Learning, 2nd Edition||Springer||2011|
|I. Goodfellow, Y, Bengio, A. Courville||Deep Learning||MIT Press||2016|
|R. C. Gonzalez, R. E. Woods||Digital Image Processing, 3rd Edition||Prentice Hall||2008|
|L. Rabiner and H. Juang||Fundamentals of speech recognition||Prentice Hall||1993|