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Mit 1.10.2022 ist die Fakultät für Physik in der TUM School of Natural Sciences mit der Webseite https://www.nat.tum.de/ aufgegangen. Unter Umstellung der bisherigen Webauftritte finden Sie weitere Informationen.

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Praktikum Applied Machine Learning

Lehrveranstaltung 0000004328 im SS 2019

Basisdaten

LV-Art Praktikum
Umfang 4 SWS
betreuende Organisation Lehrstuhl für Datenverarbeitung (Prof. Diepold)
Dozent(inn)en Matthias Kissel
Mitwirkende:
Philipp Paukner
Termine

weitere Informationen

Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).

ergänzende Hinweise 1. Deep Learning for Multimedia: Content generated for human consumption in the form of video, text, or audio, is unstructured from a machine perspective since the contained information is not readily available for processing. Information extraction from unstructured data describes therefore how one can extract the salient information from generic content in order to generate a descriptive structured representation. The thus created meta-data can then be further processed automatically, in particular for creating models explaining or predicting samples e.g. in recommendation systems. Aim of this lecture is therefore to introduce the methods, algorithms and underlying machine learning concepts for extracting information from audio, visual, and textual unstructured content using state-of-the art algorithms, especially deep learning based algorithms and architectures e.g. CNN, Autoencoder, LTSM. In addition, existing frameworks and libraries (e.g. Keras, Scikit-learn) and how to use them with audio, visual, and textual content countered in (multi-) media applications and services will be discussed. The following topics will be covered: - Why information extraction? - Introduction to deep learning - Image/video content - Object recognition - Face recognition - Character recognition (OCR) - Quality of Experience (QoE) - Audio/textual content - Automatics speech recognition (ASR) - Natural language processing (NLP) - Python eco-system of frameworks/libraries for information extraction Selected topics will be examined more in-depth during the lecture and the team oriented semester project. 2. Practical Concepts of Machine Learning: The course Practical Concepts of Machine Learning focuses on the acquiring practical skills for applying concepts of machine learning in analyzing data, which come from a wide range of data sources. We will discuss and exercise methods for ▪ planning a data collection campaign, a test procedure or measurements and experiments ▪ exploring the collected data to search for structure and meaningful patterns hidden in the data ▪ building prediction models and classifiers to capture the essence of the phenomena comprised in data ▪ exploiting human cognition and integrating domain knowledge All these methods are presented along practical examples of data processing and analyzing, covering a wide range of applications, which are representative to the field of computer engineering. The style of the course is focusing on practical aspects built on top of theoretical foundations. The presented methods directly will lead to Data Mining and Big Data topics. We will implement numerical algorithms, visualize and process the data, evaluate and validate prediction models and discuss various implementation platforms (computer architectures) for efficient data analysis.
Links TUMonline-Eintrag
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