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