Subject code AL3391 deals with semester III of B.Tech Artificial Intelligence and Data Science regarding affiliated institutions of Anna University Regulation 2021 Syllabus. In this article, you can gather certain information relevant to the Artificial Intelligence. We added the information by expertise.
We included the proper textbooks and references to assist in some way in your preparation. It will enhance your preparation and strategies to compete with the appropriate spirit with others in the examination. If you see, you can find the detailed syllabus of this subject unit-wise without leaving any topics from the unit. In this article AL3391 – Artificial Intelligence Syllabus, You can simply read the following syllabus. Hope you prepare well for the examinations. I hope this information is useful. Don’t forget to share with your friends.
If you want to know more about the syllabus of B.Tech Artificial Intelligence And Data Science connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.Tech. Artificial Intelligence And Data Science Syllabus Anna University, Regulation 2021.
Aim of Objectives:
The main objectives of this course are to:
- Learn the basic AI approaches
- Develop problem-solving agents
- Perform logical and probabilistic reasoning
AL3391 – Artificial Intelligence Syllabus
Unit I: Intelligent Agents
Introduction to AI – Agents and Environments – concept of rationality – nature of environments – structure of agents – Problem-solving agents – search algorithms – uninformed search strategies.
Unit II: Problem Solving
Heuristic search strategies – heuristic functions. Local search and optimization problems – local search in continuous space – search with non-deterministic actions – search in partially observable environments – online search agents and unknown environments.
Unit III: Game Playing And Csp
Game theory – optimal decisions in games – alpha-beta search – monte-carlo tree search – stochastic games – partially observable games. Constraint satisfaction problems – constraint propagation – backtracking search for CSP – local search for CSP – structure of CSP.
Unit IV: Logical Reasoning
Knowledge-based agents – propositional logic – propositional theorem proving – propositional model checking – agents based on propositional logic. First-order logic – syntax and semantics – knowledge representation and engineering – inferences in first-order logic – forward chaining – backward chaining – resolution.
Unit V: Probabilistic Reasoning
Acting under uncertainty – Bayesian inference – naïve Bayes models. Probabilistic reasoning – Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.
Text Books:
Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, Fourth Edition, Pearson Education, 2021.
References:
- Dan W. Patterson, “Introduction to AI and ES”, Pearson Education, 2007.
- Kevin Night, Elaine Rich, and Nair B., “Artificial Intelligence”, McGraw Hill, 2008.
- Patrick H. Winston, “Artificial Intelligence”, Third Edition, Pearson Education, 2006.
- Deepak Khemani, “Artificial Intelligence”, Tata McGraw Hill Education, 2013.
Related Posts On Semester – III:
- MA3354 Discrete Mathematics
- CS3351 Digital Principles and Computer Organization
- AD3391 Database Design and Management
- AD3351 Design and Analysis of Algorithms
- AD3301 Data Exploration and Visualization
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