IAPR

Courses on Symbolic Pattern Recognition



This page gives a summary of 70+ courses related to various aspects of symbolic pattern recognition from more than 60 universities worldwide. The courses are listed under different topic areas below. As well as this course list, we have also:

Framework

  1. General AI /Pattern Recognition

  2. Representations for Symbolic Reasoning

    1. Blackboards
    2. Decision Trees
    3. Frames
    4. Graphs
    5. Languages, Formal and Informal
    6. Modal Logic
    7. Predicate calculus
    8. Production Rules
    9. Semantic Nets
    10. Situation Calculus
  3. Methods for Symbolic Reasoning

    1. Case-based Reasoning
    2. Grammar Induction
    3. Graph matching
    4. Logic Programming
    5. Parsing
    6. Planning
    7. Rule-based System
    8. Search
    9. Theorem Proving
  4. Applications

    1. Natural Language Processing
    2. Multi-agent Systems

Details of the content

  1. General AI/Pattern Recognition

    1. Fundamentals of Artificial Intelligence, University of Edinburgh
    2. Artificial Intelligence, Heriot Watt University
    3. Pattern RecognitionMakerere University
    4. Artificial Intelligence, University of New South Wales
    5. AI: Notes for Students, Oxford University
    6. Agent Architectures, Stanford University
    7. Artificial Intelligence: Principles & Techniques, Stanford University
    8. Natural Language Processing, Stanford University
    9. Various Symbolic Systems courses, Stanford University
  2. Representations for Symbolic Reasoning

    1. Blackboards
      1. Artificial Intelligence (Lectures:10 & 11), University of Massachusetts
      2. Knowledge-based Applications Systems (Lecture 11), MIT
    2. Decision Trees
      1. Decision Trees - Lesson Plan: 2 x 1 hour lessons, Biz/Ed
      2. Machine Learning, (Lecture 8), University of Birmingham
      3. Healthcare Decision Support Systems (Lecture 6), University of Auckland
    3. Frames
      1. Theory and Practice of Knowledge Representation (Week 6), UMBC
      2. Knowledge-Based AI (Lectures 5&6), Georgia Tech University
      3. Introduction to Artificial Intelligence (week 6), University of Birmingham
      4. Automated Reasoning and AI Programming (Lecture 12), University of Sussex 
    4. Graphs
      1. Master Class on Graph Theory and Constraint Programming, INRIA
      2. Graph Theory, ETH Zurich
      3. Graph Theory , London Taught Course Centre (LTCC)
    5. Languages, Formal and Informal
      1. Regular Languages and Finite Automata, University of Cambridge
      2. Processing Natural and Formal Languages, University of Edinburgh
      3. Notes on Formal Language Theory and ParsingNational University of Ireland
      4. Formal Languages and Parsing, University of Waterloo
    6. Modal Logic
      1. Modal Logic, University of Nottingham
      2. Modal Logic, Carnegie Mellon University
      3. Introduction to Modal Logic, University of Amsterdam 
    7. Predicate calculus
      1. Logic: B1a (Lecture 8-16), University of Oxford
      2. Applied Logic for Computer Science, University of Western Ontario
      3. Artificial Intelligence (Lectures 13 & 14), Clarkson University
      4. Introduction to AI (Lectures 10&11), Brown University
    8. Production Rules
      1. Artificial Intelligence (Lecture 3), University of Manchester
      2. Artificial Intelligence (Lecture 4), Imperial College
      3. Cognition and Computation (Lecture I4), Rutgers University
    9. Semantic Nets
      1. Artificial Intelligence (Lectures 18 & 19), IIT Kharagpur
      2. Artificial Intelligence (Lecture 2), University of Colorado
      3. Artificial Intelligence (Part 4), Queen Mary University of London
    10. Situation Calculus
      1. Reasoning about Action and High-Level Programs (Lectures 1-6), York University
      2. Cognitive Robotics (Lecture1), University of New South Wales
  3. Methods for Symbolic Reasoning

    1. Case-based Reasoning
      1. Case-based Reasoning, Robert Gordon University
      2. Knowledge Management for E-Commerce (Lectures 2&5), University of Calgary
      3. Machine Learning and Case-Based Reasoning (Lectures 9-12), Norwegian University of Science and Technology (NTNU)
      4. Case-based Reasoning, University of Sofia St. Kliment Ohridski
    2. Graph matching
      1. Discrete Mathematics (Lecture 24), Princeton University
      2. Learning Graph Maching, Australian National University
      3. Graph Maching Algorithm,  National ICT Australia (NICTA)
    3. Grammar Induction
      1. Lectures on Grammatical Inference, Nantes University
      2. Viewgraphs of the PhD course: Grammatical Inference, Universidad Politecnica de Valencia (UPV)
      3. Grammatical Inference: formal and heuristic methods, Catholic University of Leuven
      4. Courses and Tutorials on Grammatical Inference, Grammatical Induction Community
    4. Logic Programming
      1. Logic Programming, University of Edinburgh
      2. Logic Programming, Cornegie Mellon University
      3. Introduction to Logic Programming, Research Institute for Symbolic Computation (RISC)
    5. Parsing
      1. Compiler Principles and Techniques, University of Utah
      2. Formal Languages and Parsing, University of Waterloo
    6. Planning
      1. Planning, Execution, and Learning Schedule, Carnegie Mellon University
      2. Automated Planning, University of Edinburgh
      3. Artificial Intelligence Planning, University of Southern California
      4. Planning and Learning, Arizona State University
      5. Planning Under Uncertainty (stochastic planning), Duke University
    7. Rule-based systems
      1. Knowledge Based Systems, University of New South Wales
      2. Knowledge Based Systems, Worcester Polytechnic Institute
      3. Expert Systems (Lectures 2 & 3), University of the West Indies
      4. Knowledge Representation and Modelling (Lecture 4), Norwegian University of Science and Technology
    8. Search
      1. Artificial Intelligence (Lectures 3 to 5: Informaed& Uninformed Search), University of Otago
      2. Introduction to Artificial Intelligence (Lectures 2 &3: Informaed& Uninformed Search), Brown University
      3. Search Algorithms, University of Paderborn
      4. Analysis of Algorithms (Lectures 7-10), Stony Brook University
      5. Search Problems and Algorithms, Helsinki University of Technology
    9. Theorem Proving
      1. Automated Theorem Proving, Carnegie Mellon University
      2. Theorem Proving - Principles, Techniques, Applications, University of New South Wales
      3. Automated Reasoning, Princeton University
  4. Applications

    1. Natural Language Processing
      1. Empirical Methods in Natural Language Processing, University of Edinburgh
      2. Natural Language Generation, University of Edinburgh
      3. Semantics and Pragmatics of Natural Language Processing, University of Edinburgh
      4. Natural Language Processing, Stanford University
      5. Information Retrieval and Web Search, Stanford University
    2. Multi-agent Systems 
      1. Agent-based Systems, University of Edinburgh
      2. Agent-based Software Engineering, University of Calgary
      3. Autonomous Agents and Multiagent Systems, Yourk University
      4. Autonomous Multiagent Systems, New York University
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