The listed learning units belong to the course Computational Learning Theory and Beyond. Do you want to access all course content?
Week 1
Welcome to the Course
Text
Pre-Course Survey
Survey
1.1 Binary Classification
Video
1.1 Binary Classification
Self-test
Excursus 1: Feature Extraction with Neural Networks
Video
1.2 Training Sequences
Video
1.2 Training Sequences
Self-test
Excursus 2: Theory of Evolutionary Algorithms
Video
1.3 Hypothesis Space
Video
1.3 Hypothesis Space
Self-test
Excursus 3: Fair Clustering
Video
1.4 Informants and Successful Learning
Video
1.4 Informants and Succesful Learning
Self-test
Excursus 4: Game Theory, Segregation and Potential Functions
Video
2.1 Learnability of Hypothesis Spaces
Video
2.1 Learnability of Hypothesis Spaces
Self-test
Excursus 5: Submodular Maximisation
Video
Learning Material
Text
Homework Week 1
Graded Test
Week 2
Welcome to Week 2
Text
2.2 LOCK Property and Storing Points
Video
2.2 LOCK Property and Storing Points
Self-test
Excursus 6: Learning from Positive Data - Overview
Video
2.3 An Iterative Learner for the Set of Halfspaces
Video
2.3 An Iterative Learner for the Set of Halfspaces
Self-test
Excursus 7: Learning from Positive Data - Additional Requirements
Video
2.4 Learning Success on the Set of Halfspaces
Video
2.4 Learning Success on the Set of Halfspaces
Self-test
Excursus 8: Embeddings
Video
2.5 Minibatch versus Batch Learners
Video
2.5 Minibatch versus Batch-Learners
Self-test
Excursus 9: Stable Matchings
Video
3.1 Consistency
Video
3.1 Consistency
Self-test
Excursus 10: Boolean Satisfiability (SAT)
Video
Learning Material
Text
Homework Week 2
Graded Test
Excursus 11: Inductive Inference
Video