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Artificial Intelligence Search Methods For Problem Solving

Team EveryEng

Team EveryEng

Mechanical Engineering

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Artificial Intelligence Search Methods For Problem Solving

  • Trainers feedback

    4

    (41 reviews)

  • Course type

    Watch to learn anytime

  • Course duration

    2344 Min

  • Course start date & time

    Access anytime

  • Language

    English

Why enroll

Completing the NPTEL course "Artificial Intelligence Search Methods For Problem Solving" can significantly enhance your career prospects in AI and related fields. By mastering search methods and problem-solving strategies, you'll develop a strong foundation in AI and be able to tackle complex challenges. This expertise will open doors to roles like AI/ML Engineer, Data Scientist, and Problem-Solving Specialist. You'll also gain a competitive edge in industries like robotics, healthcare, finance, and more. With this course, you'll be well-equipped to drive innovation and solve real-world problems, leading to accelerated career growth and new opportunities.

Opportunities that awaits you!

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Course details

This course explores the fundamental search methods used in Artificial Intelligence (AI) to solve complex problems. Students will learn various search strategies, including uninformed and informed search, local search, and constraint satisfaction. The course covers the theoretical foundations and practical applications of search methods, enabling students to design and implement efficient problem-solving algorithms.

Source: nptelhrd (Youtube Channel)
Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.

Course suitable for

  • Aerospace
  • Automotive
  • Mechanical

Key topics covered

  • Artificial Intelligence: Introduction

  • Introduction to AI

  • AI Introduction: Philosophy

  • Introduction: Philosophy

  • State Space Search - Introduction

  • Search - DFS and BFS

  • Search DFID

  • Heuristic Search

  • Hill climbing

  • Solution Space Search,Beam Search

  • TSP Greedy Methods

  • Tabu Search

  • Optimization - I (Simulated Annealing)

  • Optimization II (Genetic Algorithms)

  • Population based methods for Optimization

  • Population Based Methods II

  • Branch and Bound, Dijkstra's Algorithm

  • A* Algorithm

  • Admissibility of A*

  • A* Monotone Property, Iterative Deeping A*

  • Recursive Best First Search, Sequence Allignment

  • Pruning the Open and Closed lists

  • Problem Decomposition with Goal Trees

  • AO* Algorithm

  • Game Playing

  • Game Playing- Minimax Search

  • Game Playing - AlphaBeta

  • Game Playing-SSS *

  • Rule Based Systems

  • Inference Engines

  • Rete Algorithm

  • Planning

  • Planning FSSP, BSSP

  • Goal Stack Planning Sussman's Anomaly

  • Non-linear planning

  • Plan Space Planning

  • GraphPlan

  • Constraint Satisfaction Problems

  • CSP Continued

  • Knowlege Based Systems

  • Knowlege Based Systems PL

  • Propositional Logic

  • Resolution Refutation for PL

  • First Order Logic (FOL)

  • Reasoning in FOL

  • Backward Chaining

  • Resolution for FOL

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

Video info icon

Artificial Intelligence Search Methods For Problem Solving

48 Lectures

2344 min

  • Lesson icon

    Artificial Intelligence: Introduction

    56 min

  • Lesson icon

    Introduction to AI

    51 min

  • Lesson icon

    AI Introduction: Philosophy

    44 min

  • Lesson icon

    AI Introduction

    53 min

  • Lesson icon

    Introduction: Philosophy

    35 min

  • Lesson icon

    State Space Search - Introduction

    54 min

  • Lesson icon

    Search - DFS and BFS

    54 min

  • Lesson icon

    Search DFID

    51 min

  • Lesson icon

    Heuristic Search

    55 min

  • Lesson icon

    Hill climbing

    44 min

  • Lesson icon

    Solution Space Search,Beam Search

    42 min

  • Lesson icon

    TSP Greedy Methods

    52 min

  • Lesson icon

    Tabu Search

    38 min

  • Lesson icon

    Optimization - I (Simulated Annealing)

    48 min

  • Lesson icon

    Optimization II (Genetic Algorithms)

    53 min

  • Lesson icon

    Population based methods for Optimization

    51 min

  • Lesson icon

    Population Based Methods II

    58 min

  • Lesson icon

    Branch and Bound, Dijkstra's Algorithm

    56 min

  • Lesson icon

    A* Algorithm

    49 min

  • Lesson icon

    Admissibility of A*

    51 min

  • Lesson icon

    A* Monotone Property, Iterative Deeping A*

    46 min

  • Lesson icon

    Recursive Best First Search, Sequence Allignment

    49 min

  • Lesson icon

    Pruning the Open and Closed lists

    50 min

  • Lesson icon

    Problem Decomposition with Goal Trees

    48 min

  • Lesson icon

    AO* Algorithm

    47 min

  • Lesson icon

    Game Playing

    44 min

  • Lesson icon

    Game Playing- Minimax Search

    45 min

  • Lesson icon

    Game Playing - AlphaBeta

    47 min

  • Lesson icon

    Game Playing-SSS *

    50 min

  • Lesson icon

    Rule Based Systems

    47 min

  • Lesson icon

    Inference Engines

    41 min

  • Lesson icon

    Rete Algorithm

    49 min

  • Lesson icon

    Planning

    50 min

  • Lesson icon

    Planning FSSP, BSSP

    53 min

  • Lesson icon

    Goal Stack Planning Sussman's Anomaly

    50 min

  • Lesson icon

    Non-linear planning

    45 min

  • Lesson icon

    Plan Space Planning

    50 min

  • Lesson icon

    GraphPlan

    47 min

  • Lesson icon

    Constraint Satisfaction Problems

    52 min

  • Lesson icon

    CSP Continued

    49 min

  • Lesson icon

    Knowlege Based Systems

    53 min

  • Lesson icon

    Knowledge Based Systems PL

    49 min

  • Lesson icon

    Propositional Logic

    48 min

  • Lesson icon

    Resolution Refutation for PL

    40 min

  • Lesson icon

    First Order Logic (FOL)

    52 min

  • Lesson icon

    Reasoning in FOL

    49 min

  • Lesson icon

    Backward Chaining

    53 min

  • Lesson icon

    Resolution for FOL

    46 min

Why people choose EveryEng

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Team EveryEng

Team EveryEng

Mechanical Engineering

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