Artificial Intelligence detailed syllabus for Information Technology (IT) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the IT 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 Information Technology 7th Sem scheme and its subjects, do visit IT 7th Sem 2019 regulation scheme. The detailed syllabus of artificial intelligence is as follows.
Course Objective:
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Unit I
Intelligent Agents and Search Techniques
Agents and Environments – Good Behavior: The Concepts of Rationality – The Nature of Environments – The Structure of Agents – Problem Solving by Search – Uninformed Search – Searching with Costs – Informed State Space Search – Heuristic Search: Greedy – A* Search – Problem Reduction Search – Game Search – Constraint Satisfaction Problems.
Suggested Activities:
- Flipped classroom on structure of agents.
- Uninformed search – Searching with costs.
- Solve puzzles with uninformed and informed searches.
- Practical – Implementation of search through Python/other languages.
Suggested Evaluation Methods:
- Tutorials on various topics of the unit.
- Assignments on puzzles with uninformed and informed searches.
- Quizzes on agents, environments and search
- Evaluation of the programming exercises.
Unit II
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Unit III
Knowledge Representation
Knowledge Representation Issues – Approaches for Knowledge Representation: Simple Relational Knowledge – Inherited Knowledge – Semantic Nets – Frames – Semantic Web -Ontology.
Suggested Activities:
- Examples of knowledge representation through different methods and reasoning.
- Practical – Ontology creation using a tool like Protege.
Suggested Evaluation Methods:
- Tutorials on different topics of the unit.
- Assignments on knowledge representation through different methods and reasoning.
- Quizzes on different methods of knowledge representation.
- Evaluation of the programming exercise.
Unit IV
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Unit V
Learning and Applications
Logical Formulation of Learning – Knowledge in Learning – Explanation-based Learning -Learning using Relevance Information – Application with NLP: Developing a Simple Chatbot – Types of Chatbot.
Suggested Activities:
- Flipped classroom on knowledge in learning.
- Assignments on problem solving in learning techniques.
- Practical – Programming exercises using Python/other programming languages such as: Programming for HMM.
- Explore the available Chatbot models such as Watson and adapt to a specific domain such as Education or Customer relations.
Suggested Evaluation Methods:
- Tutorials on knowledge in learning.
- Evaluation of the programming exercise.
” Quizzes on knowledge in learning.
Course Outcome:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Text Books:
- Stuart J. Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Third Edition, Pearson Publishers, 2015.
- Elaine Rich, Kevin Knight, Shivashankar B. Nair, “Artificial Intelligence”, Third Edition, Tata McGraw-Hill Education, 2008.
References:
- Dheepak Khemani, “A first course in Artificial Intelligence”, McGraw Hill Education Pvt Ltd., NewDelhi, 2013.
- Steven Bird, Ewan Klein and Edward Loper, “Natural Language Processing with Python”, O”Reilly, 2009, https://www.nltk.org/book/.
- Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, Morgan Kaufmaan Publishers Inc; Second Edition, 2003.
- NPTEL, “Artificial Intelligence”, http://nptel.ac.in/courses/106105079/2.
- Udacity, “Introduction to Artificial Intelligence”, https://in.udacity.com/course/intro-to-artificial-intelligence–cs271.
For detailed syllabus of all other subjects of Information Technology, 2019 regulation curriculum do visit IT 7th Sem subject syllabuses for 2019 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.