Knowledge Engineering detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 6th Sem scheme and its subjects, do visit AI&ML 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&ML Professional Elective-VII syllabus scheme. The detailed syllabus of knowledge engineering is as follows.
Course Objectives:
Download the iStudy App for all syllabus and other updates.
Unit I
REASONING UNDER UNCERTAINTY
Introduction – Abductive reasoning – Probabilistic reasoning: Enumerative Probabilities -Subjective Bayesian view – Belief Functions – Baconian Probability – Fuzzy Probability -Uncertainty methods – Evidence-based reasoning – Intelligent Agent – Mixed-Initiative Reasoning – Knowledge Engineering.
Unit II
METHODOLOGY AND MODELING
Conventional Design and Development – Development tools and Reusable Ontologies – Agent Design and Development using Learning Technology – Problem Solving through Analysis and Synthesis – Inquiry-driven Analysis and Synthesis – Evidence-based Assessment – Believability Assessment – Drill-Down Analysis, Assumption-based Reasoning, and What-If Scenarios.
Unit III
Download the iStudy App for all syllabus and other updates.
Unit IV
REASONIING WITH ONTOLOGIES AND RULES
Production System Architecture – Complex Ontology-based Concepts – Reduction and Synthesis rules and the Inference Engine – Evidence-based hypothesis analysis – Rule and Ontology Matching – Partially Learned Knowledge – Reasoning with Partially Learned Knowledge.
Unit V
LEARNING AND RULE LEARNING
Machine Learning – Concepts – Generalization and Specialization Rules – Types – Formal definition of Generalization. Modelling, Learning and Problem Solving – Rule learning and Refinement – Overview – Rule Generation and Analysis – Hypothesis Learning.
Practical Exercises
- Perform operations with Evidence Based Reasoning.
- Perform Evidence based Analysis.
- Perform operations on Probability Based Reasoning.
- Perform Believability Analysis.
- Implement Rule Learning and refinement.
- Perform analysis based on learned patterns.
- Construction of Ontology for a given domain.
Course Outcomes:
Download the iStudy App for all syllabus and other updates.
Text Books:
- Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, David A. Schum, Knowledge Engineering Building Cognitive Assistants for Evidence-based Reasoning, Cambridge University Press, First Edition, 2016. (Unit 1 – Chapter 1 / Unit 2 – Chapter 3,4 / Unit 3 – Chapter 5, 6 / Unit 4 – 7 , Unit 5 – Chapter 8, 9 )
Reference Books:
- Ronald J. Brachman, Hector J. Levesque: Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.
- Ela Kumar, Knowledge Engineering, I K International Publisher House, 2018.
- John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks/Cole, Thomson Learning, 2000.
- King , Knowledge Management and Organizational Learning , Springer, 2009.
- Jay Liebowitz, Knowledge Management Learning from Knowledge Engineering, 1st Edition,2001.
For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 6th Sem, visit AI&ML 6th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.