diff --git a/Module_Modulangebot_FGI/1230525.md b/Module_Modulangebot_FGI/1230525.md new file mode 100644 index 0000000000000000000000000000000000000000..528560a635b143e5102eac71b08cedfecaecc880 --- /dev/null +++ b/Module_Modulangebot_FGI/1230525.md @@ -0,0 +1,155 @@ +## Advanced Topics in Machine Learning for Human Language Technology (1230525) + + +### 1. Allgemeine Moduldaten +*[Erläuterungen zu Abschnitt 1](../Anleitungen_Hilfen/1_Allgemeine_Moduldaten.md)* + +| Moduleigenschaft | Inhalt | +|-----------------------------|--------------------------------------------------------| +| (!) Modultitel (DE) | Advanced Topics in Machine Learning for Human Language Technology | +| (!) Modultitel (EN) | Advanced Topics in Machine Learning for Human Language Technology | +| (!) ECTS | 4 | +| (!) Gültig ab | SoSe 2024 | +| Gültig bis | | +| (!) ModulanbieterIn | Prof. Dr.-Ing. Hermann Ney | +| (!) Sprache | Englisch | +| (!) Turnus | Unregelmäßig | +| (!) Moduldauer | einsemestrig | +| (!) Modulniveau | Master | +| (!) Fachsemester | 1 | + + + +### 2. Modulveranstaltungen +*[Erläuterungen zu Abschnitt 2](../Anleitungen_Hilfen/2_Modulveranstaltungen.md)* + +| (!) Modulveranstaltungen (DE) | (!) Modulveranstaltungen (EN) | (!) ECTS | (!) Präsenzzeit (SWS) | +|-------------------------------|-------------------------------|----------|-----------------------| +| Vorlesung Advanced Topics in Machine Learning for Human Language Technology | Lecture Advanced Topics in Machine Learning for Human Language Technology | 0 | 2 | +| Übung Advanced Topics in Machine Learning for Human Language Technology | Excercise Advanced Topics in Machine Learning for Human Language Technology | 0 | 1 | +| Prüfung Advanced Topics in Machine Learning for Human Language Technology | Exam Advanced Topics in Machine Learning for Human Language Technology | 4 | 0 | + + + + +### 3. Studien- und Prüfungsleistungen +*[Erläuterungen zu Abschnitt 3](../Anleitungen_Hilfen/3_Leistungen.md)* + +**(!) Teilnahmevoraussetzungen (DE)** +Keine. + +**(!) Teilnahmevoraussetzungen (EN)** +None. + +**(!) Prüfungsbedingungen (DE)** +Klausur (100 %). Voraussetzung für die Zulassung zur Prüfung ist das Bestehen von Hausaufgaben. + +**(!) Prüfungsbedingungen (EN)** +Written Exam (100 %). Students must pass written homework to be admitted to the examination. + + + +### 4. Informationen für das Modulhandbuch +*[Erläuterungen zu Abschnitt 4](../Anleitungen_Hilfen/4_Infos_Modulhandbuch.md)* + + +**(!) Empfohlene Voraussetzungen (DE)** +Fundamentals of automatic speech recognition and machine learning, Statistical Classification and Machine Learning or further lectures on Machine Learning and/or Human Language Technology.... + +**(!) Empfohlene Voraussetzungen (EN)** +Fundamentals of automatic speech recognition and machine learning, Statistical Classification and Machine Learning or further lectures on Machine Learning and/or Human Language Technology. + +**(!) Lernziele (DE)** +Knowledge: +Today data-driven methods like machine learning and artificial neural networks (ANN) are widely used for speech and language processing, e.g. for automatic speech recognition (ASR) and machine translation. We will re-visit the evolution of these methods over the last 50 years +and will present a unifying view of their principles from a probabilistic perspective. +On successful completion of this module, students should be able to: +• describe the components and formalisms of machine learning problems in human language technology from a principled probabilistic perspective; +• state and interpret the optimization problems of machine learning for human language technology and its relation to performance evaluation. + +Skills: +They should be able to: +• apply state-of-the-art machine learning models to human language technology components; +• solve the optimization problems underlying decoding in sequence classification based on state-of-the-art machine learning components and underlying models; +• should have acquired soft skills like acquiring, reproducing and discussing knowledge about current state-of-the-art machine learning models for human language technology in a cooperative environment. + +Competences: +• understand the probabilistic interpretation of artificial neural network outputs; +• describe the relation between the task performance (e.g. word error rate in ASR) and the decision rule for generating the output sequence (e.g. Bayes decision rule); +• relate training criteria (like cross-entropy) to task performance; +• be able to model the dependencies between input and output sequences in sequence-to-sequence processing; +• know the synchronization mechanisms between input and output sequences (e.g. hidden Markov models, finite-state transducers, cross-attention); +• understand the role language modeling plays in the context of end-to-end models. + + +**(!) Lernziele (EN)** +Knowledge: +Today data-driven methods like machine learning and artificial neural networks (ANN) are widely used for speech and language processing, e.g. for automatic speech recognition (ASR) and machine translation. We will re-visit the evolution of these methods over the last 50 years +and will present a unifying view of their principles from a probabilistic perspective. +On successful completion of this module, students should be able to: +• describe the components and formalisms of machine learning problems in human language technology from a principled probabilistic perspective; +• state and interpret the optimization problems of machine learning for human language technology and its relation to performance evaluation. + +Skills: +They should be able to: +• apply state-of-the-art machine learning models to human language technology components; +• solve the optimization problems underlying decoding in sequence classification based on state-of-the-art machine learning components and underlying models; +• should have acquired soft skills like acquiring, reproducing and discussing knowledge about current state-of-the-art machine learning models for human language technology in a cooperative environment. + +Competences: +• understand the probabilistic interpretation of artificial neural network outputs; +• describe the relation between the task performance (e.g. word error rate in ASR) and the decision rule for generating the output sequence (e.g. Bayes decision rule); +• relate training criteria (like cross-entropy) to task performance; +• be able to model the dependencies between input and output sequences in sequence-to-sequence processing; +• know the synchronization mechanisms between input and output sequences (e.g. hidden Markov models, finite-state transducers, cross-attention); +• understand the role language modeling plays in the context of end-to-end models. + +**(!) Inhalt (DE)** +• Part 1: Probabilistic foundations, Bayes decision theory, probabilistic interpretation of neural networks, training criteria. +• Part 2: Sequence processing and specific ANN structures (hidden markov models, finite-state transducers, cross-attention). +• Part 3: Deep Learning and HLT tasks (automatic speech recognition, language modelling, machine translation). + +**(!) Inhalt (EN)** +• Part 1: Probabilistic foundations, Bayes decision theory, probabilistic interpretation of neural networks, training criteria. +• Part 2: Sequence processing and specific ANN structures (hidden markov models, finite-state transducers, cross-attention). +• Part 3: Deep Learning and HLT tasks (automatic speech recognition, language modelling, machine translation). + +**(!) Literatur** + + + + +### 5a. SPOen, in denen das Modul verankert werden soll + +*[Erläuterungen zu Abschnitt 5a/5b](../Anleitungen_Hilfen/5_Studiengänge.md)* + +| (!) Studiengangskürzel | (!) SPO-Version | (!) Modulbereich | +|------------------------|-----------------|------------------------------------| +| MSInf | 2009 | Wahlpflichtbereiche > Angewandte Informatik | +| MSInf | 2023 | Wahlpflichtbereiche > KI & Daten > Module aus dem Bereich KI & Daten | +| MSDaSci | 2018 | Vertiefungsbereich > Computer Science > Wahlpflichtmodule aus Modulkatalog Computer Science | +| MSDaSci | 2018 | Vertiefungsbereich > Computer Science and Mathematics > Computer Science | +| MSSSE | 2011 | Wahlpflicht > Applied Computer Science | +| MSMI | 2019 | Multimedia-Technologie | +| BSInf | 2018 | Mastervorzug | +| BSInf | 2022 | Mastervorzug | + + +### 5b. SPOen mit abweichenden Verwendungsspezifika + + +Studiengangskürzel/SPO-Version/Modulbereich: + + +Abweichende(s) Verwendungsspezifikum/-a: + + + + +### Interne Kommentare (gehören NICHT zur Moduldefinition) +Dieses Modul ersetzt das bisherige Modul Advanced Statistical Classification, Modul-Kennung 1212684. +Bitte entsprechende Anschlüsse Modellieren. +AK => GHK 184469 +PKs: +* Übung: GHK 184470 +* Prüfung: GHK 184471