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AKTU Artificial Intelligence (BCS701) Syllabus PDF – B.Tech 4th Year
Artificial Intelligence BCS701
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Official AKTU syllabus of Artificial Intelligence (BCS701) for B.Tech 4th Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Artificial Intelligence
Download PDFBCS701 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course, the student will be able to understand
| CO 1 | Understand the fundamentals of Artificial Intelligence, intelligent agents, and various approaches to problem-solving in AI. | K2 |
| CO 2 | Apply uninformed and informed search strategies, heuristics, and optimization techniques to solve classical AI problems and games. | K3 |
| CO 3 | implement logical reasoning techniques using propositional and first-order logic, including inference strategies and knowledge representation methods. | K4 |
| CO 4 | Analyze uncertainty in knowledge representation using probabilistic reasoning, fuzzy logic, and basic neural network concepts. | K4 |
| CO 5 | Evaluate and demonstrate AI applications in areas such as natural language processing (NLP), robotics, multi-agent systems, and Explainable AI (XAI) through real-world problem examples. | K5 |
Course Content
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Introduction to Artificial Intelligence & Intelligent Agents: Definition and scope of AI, History and applications of AI, Characteristics of Intelligent Agents, Types of agents and environments, Agent architecture, Problem Solving Approach to Typical AI problems, Problem-solving agents. Example problems and approaches. | 08 |
| II | Problem Solving & Search Strategies: Uninformed Search Strategies: BFS, DFS, Iterative Deepening, Informed Search Strategies: Greedy Best-First Search, A* Search, Heuristics and Optimization, Hill Climbing, Simulated Annealing, Constraint Satisfaction Problems, Game Playing: Min-max, Alpha-Beta Pruning, Stochastic & Partially Observable Games. | 08 |
| III | Knowledge Representation & Reasoning: Propositional and First Order Logic, Syntax, Semantics, and Inference, Knowledge-based agents: Wumpus world, Logic Programming using Prolog, Forward and Backward Chaining, Resolution, Ontological Engineering and Reasoning. | 08 |
| IV | Uncertainty & Learning Techniques: Introduction to uncertainty and probabilistic reasoning, Bayes' Rule, Bayesian Networks, Fuzzy logic and handling imprecision, Neural Networks (basics only): Perceptron, Backpropagation (intro level), Fundamentals of Machine Learning in AI context, Introduction to supervised and unsupervised learning. | 08 |
| V | Applications of AI & Multi-Agent Systems: Natural Language Processing, Machine Translation, Information Retrieval and Extraction, Robotics: Perception, Planning, and Motion, Speech Recognition, Software Agents: Architecture, Communication, Trust, Multi-agent Negotiation and Reputation. Explainable AI (XAI) – Importance of interpretability, techniques for explaining black-box models, trust in AI, case studies in NLP and vision. | 08 |
RecommendedTextbooks:
- Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2022
- Ivan Bratko, Prolog: Programming for Artificial Intelligence, 4th Edition, Addison-Wesley
- Nils J. Nilsson, The Quest for Artificial Intelligence, Cambridge University Press
- David Poole & Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge Press
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