Smarter Notes for Smarter Minds...!
Explore Video LecturesGame Theory, Optimal Decisions in Games, Heuristic Alpha–Beta Tree Search, Monte Carlo Tree Search, Stochastic Games, Partially Observable Games, Limitations of Game Search Algorithms, Constraint Satisfaction Problems (CSP), Constraint Propagation: Inference in CSPs, Backtracking Search for CSPs.
Download PPTLogical Agents, Knowledge-Based Agents, The Wumpus World, Logic, Propositional Logic: A Very Simple Logic, Propositional Theorem Proving, Effective Propositional Model Checking, Agents Based on Propositional Logic, First-Order Logic, Representation Revisited, Syntax and Semantics of First-Order Logic, Using First-Order Logic, Knowledge Engineering in First-Order Logic.
Download PPTInference in First-Order Logic, Propositional vs. First-Order Inference, Unification and First-Order Inference, Forward Chaining, Backward Chaining, Resolution, Knowledge Representation, Ontological Engineering, Categories and Objects, Events, Mental Objects and Modal Logic, Reasoning Systems for Categories, Reasoning with Default Information
Download PPTAutomated Planning, Classical Planning, Algorithms for Classical Planning, Heuristics for Planning, Hierarchical Planning, Planning and Acting in Nondeterministic Domains, Time, Schedules, and Resources, Analysis of Planning Approaches, Limits of AI, Ethics of AI, Future of AI, AI Components, AI Architectures.
Download PPTImplement depth first search algorithm and Breadth First Search algorithm.
Download PDFImplement a solution for a Constraint Satisfaction Problem using Backtracking for n-queens problem.
Download PDFImplement a solution for a Constraint Satisfaction Problem using Branch and Bound for n-queens problem.
Download PDFDevelop an elementary chatbot for any suitable customer interaction application.
Download PDFThis playlist covers the fundamental concepts and history of Artificial Intelligence, including definitions, applications, and the different types of AI systems. It introduces the basics that every AI student should know before diving deeper into the subject.
Explore problem-solving techniques in AI including search algorithms like uninformed and informed search, heuristic functions, and state-space representations. This unit provides hands-on understanding of how AI approaches solving complex problems efficiently.
Dive into game theory, adversarial search techniques like Minimax and Alpha-Beta pruning, and their applications in game playing AI. Understand how AI makes optimal decisions in competitive environments through strategic planning and heuristic evaluation.
Learn about knowledge representation and reasoning, logical agents, propositional and first-order logic, and knowledge engineering. This unit focuses on how AI systems represent information and derive conclusions logically.
Understand different reasoning methods used in AI including inference in first-order logic, forward and backward chaining, resolution, and handling default reasoning. This unit emphasizes how AI systems draw conclusions and make decisions based on knowledge.
Explore automated planning concepts, classical and hierarchical planning algorithms, heuristics, and planning in nondeterministic domains. Also, learn about AI ethics, architectures, and future directions related to intelligent planning systems.
Download free handwritten notes that explain AI problem-solving techniques like search trees, uninformed and informed search, and heuristics. These notes are ideal for understanding how AI tackles real-world problems step-by-step.
Download PDFThese notes simplify game theory concepts used in AI, such as Minimax, Alpha-Beta Pruning, and strategy formulation. A must-have resource to understand how AI performs in competitive environments like games and simulations.
Download PDFUnderstand how AI stores and processes information using propositional logic, first-order logic, and semantic networks. These free notes help you master logical reasoning and the structure of intelligent systems in a simple and easy-to-understand format.
Download PDFThese notes cover inference techniques, default reasoning, and rule-based systems in AI. Ideal for quick preparation, the notes explain forward/backward chaining and resolution techniques in a visually engaging handwritten format.
Download PDFThe AI subject covers units on intelligent agents, problem-solving techniques, adversarial search, knowledge representation, reasoning, planning, and the ethical and future scope of AI.
✅ Yes, all AI notes, PPTs, practical PDFs, and question papers provided on this website are free to download for students' academic use.
Yes, all materials are designed based on the latest 2019 pattern syllabus for SPPU Computer Engineering students.
You can find Insem and Endsem AI question papers from 2022, 2023, and 2024 in the "Question Papers" section on this page.
Yes. Every AI practical is accompanied by a video explanation and a downloadable solution PDF for better understanding.
Practicals include BFS, DFS, Greedy Algorithm, N-Queens Problem, Chatbot development, and Expert Systems.
Absolutely! These notes are curated to help students revise faster and understand key concepts important for university exams.
Click the "Download PDF" links under each unit or section. The files will open in a new tab or directly download based on your browser settings.
SPPU students, AI project developers, and those preparing for AI-related interviews or competitive exams can benefit from this content.
The site is regularly updated with new question papers, improved notes, and updated practicals based on the latest academic needs and university changes.