626.017 (24S) Selected Topics in Machine Learning
Overview
- Lecturer
- Course title german Selected Topics in Machine Learning
- Type Lecture - Course (continuous assessment course )
- Course model Blended learning course
- Online proportion 25%
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 16 (30 max.)
- Organisational unit
- Language of instruction Englisch
- possible language(s) of the assessment English
- Course begins on 04.06.2024
- eLearning Go to Moodle course
Time and place
Course Information
Teaching methodology including the use of eLearning tools
Lectures, practical exercises, and an optional project possibly chosen by the student and a topic of the student's choice.
Course content
Reinforcement learning
Reinforcement learning is about making sequences of decisions
Stunning achievements of reinforcement learning
How to find good sequences of decisions in an unknown domain through exploration and learning?
Delayed rewards, long-term benefits of decisions, exploration and exploitation
Improving decision policy through exploration
Generalizing what has been learned
Learning from examples and background knowledge
How to use prior knowledge in Machine Learning?
Learning in logic – Inductive Logic Programming (ILP)
Algorithms for learning programs from examples in ILP
Discovering new abstract concepts
Learning qualitative models with applications in robotics
How to model qualitatively, avoiding numbers?
Reasoning and simulation with qualitative models
Learning qualitative models from observations
Learning and planning of robot tasks: rescue robot, cart-pole balancing, humanoid robot, quadcopter
Learning from noisy data
Problems with noise in learning data
Key ideas to cope with noise: simpler models are often better
Algorithms for learning decision trees from noisy data
How to estimate probabilities in machine learning correctly?
Argument-Based Machine Learning (ABML)
Human expert may help learning by annotating training examples with arguments
An algorithm for learning rules from argumented examples
Discovering problem structure with function decomposition
The idea of structuring the learning problem with function decomposition
Discovering structure with HINT algorithm
Improving accuracy and interpretability by structure learning
Examination information
Examination methodology
Written exam, possible bonus points for optional project
Examination topic(s)
Content actually covered in lectures
Assessment criteria / Standards of assessment for examinations
Points scored at written exam, adding bonus points from optional project
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Thematic Doctoral Programme Informatics
(SKZ: ---, Version: 17W.1)
-
Subject: Informatics
(Compulsory subject)
-
Informatics (
0.0h XX / 0.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
-
Informatics (
0.0h XX / 0.0 ECTS)
-
Subject: Informatics
(Compulsory subject)
- Master's degree programme Informatics
(SKZ: 911, Version: 19W.2)
-
Subject: Artificial Intelligence
(Compulsory elective)
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2. Semester empfohlen
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
-
Subject: Artificial Intelligence
(Compulsory elective)
- Master's degree programme Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Subject: Specialisation in Artificial Intelligence and Cybersecurity
(Compulsory elective)
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 2., 3. Semester empfohlen
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Subject: Specialisation in Artificial Intelligence and Cybersecurity
(Compulsory elective)
- Doctoral programme Doctoral programme in Technical Sciences
(SKZ: 700, Version: 18W.1)
-
Subject: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Compulsory subject)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
0.0h XX / 32.0 ECTS)
- 626.017 Selected Topics in Machine Learning (2.0h VC / 4.0 ECTS)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
0.0h XX / 32.0 ECTS)
-
Subject: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Compulsory subject)
Equivalent courses for counting the examination attempts
-
Sommersemester 2023
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2022
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2020
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2019
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2018
- 623.131 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2017
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2016
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2015
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2014
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2013
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
-
Sommersemester 2012
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
-
Sommersemester 2011
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)