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Machine Learning in Signal Processing (MLSIP)- Lecturer
- Prof. Dr. Veniamin Morgenshtern
- Details
- Vorlesung
3 cred.h, ECTS studies, ECTS credits: 5, Sprache Englisch
Time and place: Wed 8:15 - 9:45, H5; Fri 12:15 - 13:45, H5
- Fields of study
- PF ASC-MA 1-4 (ECTS-Credits: 5)
WF CME-MA 1-4 (ECTS-Credits: 5)
WPF IuK-MA-ES 1-4 (ECTS-Credits: 5)
WPF IuK-MA-MMS 1-4 (ECTS-Credits: 5)
WPF IuK-MA-KOMÜ 1-4 (ECTS-Credits: 5)
WPF CE-MA-TA-IT 1-4 (ECTS-Credits: 5)
- Contents
- This course is an introduction into statistical machine learning and artificial intelligence. The special emphasis is on applications to modern signal processing problems. The course is focused on design principles of machine learning algorithms. First we will study basic methods for regression and classification: linear regression, logistic regression, the nearest neighbors algorithm. Based on these examples, we will discuss the fundamental trade-off between the flexibility of the model and the ability to fit the model based on the moderate amount of training data. We will contrast learning in high-dimensional spaces vs. learning in low dimensional spaces. Next, we will study methods that help make linear models flexible: polynomial features and splines. When these tools are used, regularization is crucial. Next, we will discuss structured signal representations: short-time Fourier transform, Gabor frames, wavelets. We will focus on the importance of sparsity in signal representations. This will lead us to compressed sensing and to other modern convex-optimization-based methods for signal denoising, reconstruction, and compression. We will review key concepts in convex optimization, study the LASSO, support vector machines, the idea of kernels. The last part of the course will focus on the breakthrough new technologies for computer vision: HOG and SIFT features for image recognition and the deep learning. Time permitting we will discuss unsupervised learning algorithms: principle component analysis, k-means clustering, mixture of Gaussians, the EM algorithm.
The course contains exercises: 30% mathematical and 70% programming in Python. You will be asked to implement basic machine learning and signal processing algorithms yourself. For more advanced algorithms, you will practice using powerful numerical and optimization libraries (numpy, cvxpy, scikit-learn, pywavelets, pytorch).
- Recommended literature
- T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Chapters 1–7.
A. Ng: Lecture notes and materials for Stanford CS229 class. Lecture Notes and Exercises.
M. Kon: Lecture notes on basics of wavelets.
M. Nielsen: Neural networks and deep learning.
- ECTS information:
- Title:
- Machine Learning in Signal Processing
- Credits: 5
- Contents
- This course is an introduction into statistical machine learning and artificial intelligence. The special emphasis is on applications to modern signal processing problems. The course is focused on design principles of machine learning algorithms. First we will study basic methods for regression and classification: linear regression, logistic regression, the nearest neighbors algorithm. Based on these examples, we will discuss the fundamental trade-off between the flexibility of the model and the ability to fit the model based on the moderate amount of training data. We will contrast learning in high-dimensional spaces vs. learning in low dimensional spaces. Next, we will study methods that help make linear models flexible: polynomial features and splines. When these tools are used, regularization is crucial. Next, we will discuss structured signal representations: short-time Fourier transform, Gabor frames, wavelets. We will focus on the importance of sparsity in signal representations. This will lead us to compressed sensing and to other modern convex-optimization-based methods for signal denoising, reconstruction, and compression. We will review key concepts in convex optimization, study the LASSO, support vector machines, the idea of kernels. The last part of the course will focus on the breakthrough new technologies for computer vision: HOG and SIFT features for image recognition and the deep learning. Time permitting we will discuss unsupervised learning algorithms: principle component analysis, k-means clustering, mixture of Gaussians, the EM algorithm.
The course contains exercises: 30% mathematical and 70% programming in Python. You will be asked to implement basic machine learning and signal processing algorithms yourself. For more advanced algorithms, you will practice using powerful numerical and optimization libraries (numpy, cvxpy, scikit-learn, pywavelets, pytorch).
- Literature
- T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Chapters 1–7.
A. Ng: Lecture notes and materials for Stanford CS229 class. Lecture Notes and Exercises.
M. Kon: Lecture notes on basics of wavelets.
M. Nielsen: Neural networks and deep learning.
- Additional information
- Expected participants: 13, Maximale Teilnehmerzahl: 30
www: https://lms.tf.fau.de/studium-und-lehre/lehrveranstaltungen/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2018/2019:
- Machine Learning in Signal Processing (MLSIP)
- Department: Chair of Multimedia Communications and Signal Processing (Prof. Dr. Kaup)
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