Lecture
Knowledge Representation: Introduction
Related lectures (130)
Neuro-symbolic Representations: Commonsense Knowledge & Reasoning
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Neuro-symbolic Representations: Commonsense Knowledge & Reasoning
Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.
AI/ML in Enterprises
Explores how AI/ML is shaping the future workplace, focusing on enterprise systems and processes, and discusses the current state of AI/ML adoption in enterprises.
Ethics and Law of AI
Delves into different ethical theories and thought experiments in the context of AI, exploring the challenges of defining AI.
Inference Engines: Resolution and Horn Clauses
Covers inference engines based on resolution, Horn clauses, filtering, and unification in artificial intelligence.
Predicate Logic: Quantifiers and Normal Forms
Explores predicate logic, focusing on quantifiers and normal forms, emphasizing the importance of finding witnesses and counterexamples.
Predicate Logic: Quantifiers, CNF, DNF
Covers Predicate Logic, focusing on Quantifiers, CNF, and DNF.
Knowledge Inference: Entity Disambiguation and Graph Embeddings
Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.
Stochastic Block Model: Community Detection
Covers the Stochastic Block Model for community detection.
Quantum Source Coding
Covers entropic notions in quantum sources, Shannon entropy, Von Neumann entropy, and source coding.