Artificial Intelligence: A Fundamental Guide

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AI & ML Artificial Intelligence: A Fundamental Guide

Course Speciality

-Get a deeper knowledge about Artificial Intelligence with this course
-Learn the various aspects of Artificial Intelligence and its use in our lives
-Get a detailed learning about AI and understand how these technologies can help solve challenging problems.
Artificial Intelligence: A Fundamental Guide is created by AI & ML teachers & experts for students preparing for AI & ML syllabus. Artificial Intelligence: A Fundamental Guide will help everyone preparing for AI & ML syllabus with already 307 students enrolled. Artificial Intelligence: A Fundamental Guide is the best book for AI & ML. You can download Free Artificial Intelligence: A Fundamental Guide pdf from this course as well. This also contains AI & ML slides including Artificial Intelligence: A Fundamental Guide ppt. AI & ML 307 for Artificial Intelligence: A Fundamental Guide syllabus are also available any AI & ML entrance exam. With AI & ML exam 2019 coming close, we have covered AI & ML exam 2018, 2017 & 2016 as well to get you a perfect result for AI & ML. This is the best Artificial Intelligence: A Fundamental Guide e-book even including all AI & ML sample papers and study material from the best teachers and experts from all over the country. All AI & ML notifications will be updated in this and you can apply for any AI & ML form after this and expect a great result after studying from this course!

Course content

  1. Artificial Intelligence: A Fundamental Guide

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10. Introduction to Learning; Nearest Neighbors

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14. Learning: Sparse Spaces; Phonology

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7. Constraints: Interpreting Line Drawings

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18. Representations: Classes; Trajectories; Transitions

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6. Search: Games; Minimax; and Alpha-Beta

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12a: Neural Nets

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4. Search: Depth-First; Hill Climbing; Beam

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Mega-R7. Near Misses; Arch Learning

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9. Constraints: Visual Object Recognition

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17. Learning: Boosting

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19. Architectures: GPS; SOAR; Subsumption; Society of Mind

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3. Reasoning: Goal Trees and Rule-Based Expert Systems

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12b: Deep Neural Nets

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23. Model Merging; Cross-Modal Coupling; Course Summary

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Mega-R6. Boosting

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13. Learning: Genetic Algorithms

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Mega-R5. Support Vector Machines

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2. Reasoning: Goal Trees and Problem Solving

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8. Constraints: Search; Domain Reduction

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16. Learning: Support Vector Machines

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22. Probabilistic Inference II

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1. Introduction and Scope

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21. Probabilistic Inference I

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15. Learning: Near Misses; Felicity Conditions

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5. Search: Optimal; Branch and Bound; A*

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Mega-R3. Games; Minimax; Alpha-Beta

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Mega-R4. Neural Nets

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Mega-R1. Rule-Based Systems

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Mega-R2. Basic Search; Optimal Search

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