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Voting and verification process using a cast-as-intended verifiability approach with a second device
Voting and verification process using a cast-as-intended verifiability approach with a second device
Tobias Hilt, Benjamin Berens, Tomasz Truderung, Margarita Udovychenko, Stephan Neumann …
Nature-inspired optimization methods SS 2023, Teil 16
Nature-inspired optimization methods SS 2023, Teil 16
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 15
Nature-inspired optimization methods SS 2023, Teil 15
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 14
Nature-inspired optimization methods SS 2023, Teil 14
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 13
Nature-inspired optimization methods SS 2023, Teil 13
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 12
Nature-inspired optimization methods SS 2023, Teil 12
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 11
Nature-inspired optimization methods SS 2023, Teil 11
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 10
Nature-inspired optimization methods SS 2023, Teil 10
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 9
Nature-inspired optimization methods SS 2023, Teil 9
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 8
Nature-inspired optimization methods SS 2023, Teil 8
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 7
Nature-inspired optimization methods SS 2023, Teil 7
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 6
Nature-inspired optimization methods SS 2023, Teil 6
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 1
Nature-inspired optimization methods SS 2023, Teil 1
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 2
Nature-inspired optimization methods SS 2023, Teil 2
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 5
Nature-inspired optimization methods SS 2023, Teil 5
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 4
Nature-inspired optimization methods SS 2023, Teil 4
Pradyumn Shukla
Nature-inspired optimization methods SS 2023, Teil 3
Nature-inspired optimization methods SS 2023, Teil 3
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 12
Computational Economics WS 2022/23, Teil 12
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 8
Computational Economics WS 2022/23, Teil 8
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 7
Computational Economics WS 2022/23, Teil 7
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 5
Computational Economics WS 2022/23, Teil 5
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 4
Computational Economics WS 2022/23, Teil 4
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 10
Computational Economics WS 2022/23, Teil 10
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 9
Computational Economics WS 2022/23, Teil 9
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 6
Computational Economics WS 2022/23, Teil 6
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 3
Computational Economics WS 2022/23, Teil 3
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 2
Computational Economics WS 2022/23, Teil 2
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 11
Computational Economics WS 2022/23, Teil 11
Pradyumn Shukla
Computational Economics WS 2022/23, Teil 1
Computational Economics WS 2022/23, Teil 1
Pradyumn Shukla
New Work - Erfahrungen der Piloten - Pilotprojekt AIFB
New Work - Erfahrungen der Piloten - Pilotprojekt AIFB
Isabelle Hartmann-Dietsch, Monika Landgraf
Nature-inspired optimization methods SS 2022 : Chapter: Evolutionary algorithms
Nature-inspired optimization methods SS 2022 : Chapter: Evolutionary algorithms
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Modelling fairness in resource allocation problems
Nature-inspired optimization methods SS 2022 : Modelling fairness in resource allocation problems
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Chapter: Constraint handling techniques
Nature-inspired optimization methods SS 2022 : Chapter: Constraint handling techniques
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : User preferences, distributed computing
Nature-inspired optimization methods SS 2022 : User preferences, distributed computing
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Chapter: An Algorithmic Framework for Many-Objective-Optimization
Nature-inspired optimization methods SS 2022 : Chapter: An Algorithmic Framework for Many-Objective-Optimization
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Robust Design Optimization
Nature-inspired optimization methods SS 2022 : Robust Design Optimization
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Multi-objective Evolutionary Algorithms
Nature-inspired optimization methods SS 2022 : Multi-objective Evolutionary Algorithms
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Search operators
Nature-inspired optimization methods SS 2022 : Search operators
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Chapter: Representation
Nature-inspired optimization methods SS 2022 : Chapter: Representation
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima
Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Problem Solving and Basic Concepts II. Basic Concepts
Nature-inspired optimization methods SS 2022 : Problem Solving and Basic Concepts II. Basic Concepts
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima II. Tabu Search
Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima II. Tabu Search
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Chapter 1: Problem Solving and Basic Concepts
Nature-inspired optimization methods SS 2022 : Chapter 1: Problem Solving and Basic Concepts
Pradyumn Shukla
Nature-inspired optimization methods SS 2022 : Introductory lecture
Nature-inspired optimization methods SS 2022 : Introductory lecture
Pradyumn Shukla
Computational Economics WS 2021/2022, 08.02.2022
Computational Economics WS 2021/2022, 08.02.2022
Pradyumn Shukla
Computational Economics WS 2021/2022, 28.01.2022
Computational Economics WS 2021/2022, 28.01.2022
Pradyumn Shukla
Computational Economics WS 2021/2022, 25.01.2022
Computational Economics WS 2021/2022, 25.01.2022
Pradyumn Shukla
Computational Economics WS 2021/2022, 17.11.2021
Computational Economics WS 2021/2022, 17.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 15.11.2021
Computational Economics WS 2021/2022, 15.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 15.11.2021
Computational Economics WS 2021/2022, 15.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 12.11.2021
Computational Economics WS 2021/2022, 12.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 11.11.2021
Computational Economics WS 2021/2022, 11.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 10.11.2021
Computational Economics WS 2021/2022, 10.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 15.11.2021
Computational Economics WS 2021/2022, 15.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 16.11.2021
Computational Economics WS 2021/2022, 16.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 09.11.2021
Computational Economics WS 2021/2022, 09.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 16.11.2021
Computational Economics WS 2021/2022, 16.11.2021
Pradyumn Shukla
Computational Economics WS 2021/2022, 08.11.2021
Computational Economics WS 2021/2022, 08.11.2021
Pradyumn Shukla
13: Nature-inspired optimization methods, Vorlesung SS 2021
13: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
11: Nature-inspired optimization methods, Vorlesung SS 2021
11: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
10: Nature-inspired optimization methods, Vorlesung SS 2021
10: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
09: Nature-inspired optimization methods, Vorlesung SS 2021
09: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
17: Nature-inspired optimization methods, Vorlesung SS 2021
17: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
16: Nature-inspired optimization methods, Vorlesung SS 2021
16: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
15: Nature-inspired optimization methods, Vorlesung SS 2021
15: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
14: Nature-inspired optimization methods, Vorlesung SS 2021
14: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
12: Nature-inspired optimization methods, Vorlesung SS 2021
12: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
08: Nature-inspired optimization methods, Vorlesung SS 2021
08: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
07: Nature-inspired optimization methods, Vorlesung SS 2021
07: Nature-inspired optimization methods, Vorlesung SS 2021
Pradyumn Shukla
05: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021
05: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021
Pradyumn Shukla
06: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021
06: Nature-inspired optimization methods, Vorlesung SS 2021, 21.05.2021
Pradyumn Shukla
04: Nature-inspired optimization methods. Vorlesung SS 2021, 20.05.2021
04: Nature-inspired optimization methods. Vorlesung SS 2021, 20.05.2021
Pradyumn Shukla
03: Nature-inspired optimization methods, Vorlesung SS 2021, 19.05.2021
03: Nature-inspired optimization methods, Vorlesung SS 2021, 19.05.2021
Pradyumn Shukla
02: Nature-inspired optimization methods, Vorlesung SS 2021, 18.05.2021
02: Nature-inspired optimization methods, Vorlesung SS 2021, 18.05.2021
Pradyumn Shukla
01: Nature-inspired optimization methods, Vorlesung SS 2021, 17.05.2021
01: Nature-inspired optimization methods, Vorlesung SS 2021, 17.05.2021
Pradyumn Shukla
11: Search operators, Vorlesung, WS 2020/21, 08.02.2021
11: Search operators, Vorlesung, WS 2020/21, 08.02.2021
Pradyumn Shukla
10: Application 1: Fairness Modelling in Allocation Problems, Vorlesung, WS 2020/21, 07.01.2021
10: Application 1: Fairness Modelling in Allocation Problems, Vorlesung, WS 2020/21, 07.01.2021
Pradyumn Shukla
9: Robust Optimization, Vorlesung, WS 2020/21, 23.12.2020
9: Robust Optimization, Vorlesung, WS 2020/21, 23.12.2020
Pradyumn Shukla
7: Understanding how GAs work. Vorlesung WS, 2020/21, 22.12.2020
7: Understanding how GAs work. Vorlesung WS, 2020/21, 22.12.2020
Pradyumn Shukla
4: Evolutionary Algorithms: Inspiration, Vorlesung, WS 2020/21, 17.12.2020
4: Evolutionary Algorithms: Inspiration, Vorlesung, WS 2020/21, 17.12.2020
Pradyumn Shukla
2: Statistical Foundations: Monte Carlo and Quasi Monte Carlo Methods, Vorlesung, WS 2020/21, 15.12.2020
2: Statistical Foundations: Monte Carlo and Quasi Monte Carlo Methods, Vorlesung, WS 2020/21, 15.12.2020
Pradyumn Shukla
8: Multi-objective Algorithms, Vorlesung, WS 2020/21, 23.12.2020
8: Multi-objective Algorithms, Vorlesung, WS 2020/21, 23.12.2020
Pradyumn Shukla
6: Evolutionary Algorithms: Selective Pressure, Vorlesung, WS 2020/21, 21.12.2020
6: Evolutionary Algorithms: Selective Pressure, Vorlesung, WS 2020/21, 21.12.2020
Pradyumn Shukla
5: Evolutionary Algorithms: Selection. Vorlesung, WS 2020/21, 18.12.2020
5: Evolutionary Algorithms: Selection. Vorlesung, WS 2020/21, 18.12.2020
Pradyumn Shukla
3: Statistical Foundations: Analysis of Computer Experiments, Vorlesung, WS 2020/21, 16.12.2020
3: Statistical Foundations: Analysis of Computer Experiments, Vorlesung, WS 2020/21, 16.12.2020
Pradyumn Shukla
1: Statistical Foundations: Random Number Generation, Vorlesung, WS 2020/21, 14.12.2020
1: Statistical Foundations: Random Number Generation, Vorlesung, WS 2020/21, 14.12.2020
Pradyumn Shukla
IT-basierte Services für das Student-Lifecycle Management
IT-basierte Services für das Student-Lifecycle Management
KIT-Teilprojekte Lehre^Forschung PLUS in Zusammenarbeit mit den Organisator*innen der Campustage 2020
Chapter: Modelling fairness in resource allocation problems, Vorlesung SS 2020, 16.07.2020
Chapter: Modelling fairness in resource allocation problems, Vorlesung SS 2020, 16.07.2020
Pradyumn Shukla
Chapter: Constraint handling techniques, Vorlesung SS 2020, 09.07.2020
Chapter: Constraint handling techniques, Vorlesung SS 2020, 09.07.2020
Pradyumn Shukla
Chapter: Robust design optimization, Vorlesung SS 2020, 11.07.2020
Chapter: Robust design optimization, Vorlesung SS 2020, 11.07.2020
Pradyumn Shukla
Chapter: An algorithmic framework for many-objective optimization, Vorlesung SS 2020, 10.07.2020
Chapter: An algorithmic framework for many-objective optimization, Vorlesung SS 2020, 10.07.2020
Pradyumn Shukla
Chapter: User preferences, distributed computing, Vorlesung SS 2020, 05.07.2020
Chapter: User preferences, distributed computing, Vorlesung SS 2020, 05.07.2020
Pradyumn Shukla
Chapter: Multi-objective Evolutionary Algorithms, Vorlesung SS 2020, 04.07.2020
Chapter: Multi-objective Evolutionary Algorithms, Vorlesung SS 2020, 04.07.2020
Pradyumn Shukla
Chapter: Evolutionary algorithms, Part II, Vorlesung SS 2020, 10.06.2020
Chapter: Evolutionary algorithms, Part II, Vorlesung SS 2020, 10.06.2020
Pradyumn Shukla
Chapter: Evolutionary algorithms, Part I, Vorlesung SS 2020, 30.05.2020
Chapter: Evolutionary algorithms, Part I, Vorlesung SS 2020, 30.05.2020
Pradyumn Shukla
Chapter: Escaping Local Minima. II. Tabu Search, Vorlesung SS 2020, 29.05.2020
Chapter: Escaping Local Minima. II. Tabu Search, Vorlesung SS 2020, 29.05.2020
Pradyumn Shukla
Chapter: Escaping Local Minima. I. Simulated Annealing, Vorlesung SS 2020, 17.05.2020
Chapter: Escaping Local Minima. I. Simulated Annealing, Vorlesung SS 2020, 17.05.2020
Pradyumn Shukla
Chapter 1: Problem Solving and Basic Concepts. I. Problem Solving, Vorlesung SS 2020, 15.05.2020
Chapter 1: Problem Solving and Basic Concepts. I. Problem Solving, Vorlesung SS 2020, 15.05.2020
Pradyumn Shukla
Chapter: Representation, Vorlesung SS 2020, 17.05.2020
Chapter: Representation, Vorlesung SS 2020, 17.05.2020
Pradyumn Shukla
Chapter 1: Problem Solving and Basic Concepts. II. Basic Concepts, Vorlesung SS 2020, 16.05.2020
Chapter 1: Problem Solving and Basic Concepts. II. Basic Concepts, Vorlesung SS 2020, 16.05.2020
Pradyumn Shukla
Chapter 0: Nature-inspired optimization methods, Vorlesung SS 2020, 03.05.2020
Chapter 0: Nature-inspired optimization methods, Vorlesung SS 2020, 03.05.2020
Pradyumn Shukla
07: Entscheidungsbäume: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 29.11.2019
07: Entscheidungsbäume: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 29.11.2019
Johann Marius Zöllner
11: Hidden Markov Modelle: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 10.01.2020
11: Hidden Markov Modelle: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 10.01.2020
Johann Marius Zöllner
13: Sum Product Networks: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 31.01.2020
13: Sum Product Networks: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 31.01.2020
Johann Marius Zöllner
12: Unüberwachte Lernverfahren: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 17.01.2020
12: Unüberwachte Lernverfahren: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 17.01.2020
Johann Marius Zöllner
10: Lernen nach Bayes: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 20.12.2019
10: Lernen nach Bayes: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 20.12.2019
Johann Marius Zöllner
08: Grundlagen des Reinforcement Learning (Wiederholung): Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 13.12.2019
08: Grundlagen des Reinforcement Learning (Wiederholung): Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 13.12.2019
Johann Marius Zöllner
09: Erweiterte Verfahren des Reinforcement Learning: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 13.12.2019
09: Erweiterte Verfahren des Reinforcement Learning: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 13.12.2019
Johann Marius Zöllner
06: Support Vector Machine: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 22.11.2019
06: Support Vector Machine: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 22.11.2019
Johann Marius Zöllner
08: Grundlagen des Reinforcement Learning: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 06.12.2019
08: Grundlagen des Reinforcement Learning: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 06.12.2019
Johann Marius Zöllner
04: Neuronale Netze: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 08.11.2019
04: Neuronale Netze: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 08.11.2019
Johann Marius Zöllner
05: CNN: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 15.11.2019
05: CNN: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 15.11.2019
Johann Marius Zöllner
03: Lerntheorie: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 25.10.2019
03: Lerntheorie: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 25.10.2019
Johann Marius Zöllner
02: Induktives Lernen: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 18.10.2019
02: Induktives Lernen: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 18.10.2019
Johann Marius Zöllner
01: Einführung: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 18.10.2019
01: Einführung: Maschinelles Lernen 1 - Grundverfahren, Vorlesung, WS 2019/20, 18.10.2019
Johann Marius Zöllner
12: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2019, 19.07.2019
12: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2019, 19.07.2019
Johann Marius Zöllner, Christian Hubschneider
10: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2019, 21.06.2019
10: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2019, 21.06.2019
Johann Marius Zöllner
09: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2019, 14.06.2019
09: Maschinelles Lernen 2 - Fortgeschrittene Verfahren, Vorlesung, SS 2019, 14.06.2019
Johann Marius Zöllner
12: Grundlagen der Informatik I, Vorlesung, SS 2019, 16.07.2019
12: Grundlagen der Informatik I, Vorlesung, SS 2019, 16.07.2019
York Sure-Vetter, Michael Färber
20: Höhere Mathematik II für die Fachrichtungen Maschinenbau, Geodäsie, Materialwissenschaft und Werkstofftechnik, Vorlesung, SS 2019, 16.07.2019
20: Höhere Mathematik II für die Fachrichtungen Maschinenbau, Geodäsie, Materialwissenschaft und Werkstofftechnik, Vorlesung, SS 2019, 16.07.2019
Frank Hettlich