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Artificial Intelligence Computer Science

Course Information

Difficulty: Intermediate

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Course Instructor

Dr.Mahendra Kanojia Dr.Mahendra Kanojia Author
 

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Practical

Practical 1: Aim: Implement Breadth first search algorithm for Romanian map problem.
Lesson 1 of 9 within section Practical.
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Practical 2: Aim: Implement Iterative deep depth first search for Romanian map problem.
Lesson 2 of 9 within section Practical.
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Practical 3: Aim: Implement A* search algorithm for Romanian map problem.
Lesson 3 of 9 within section Practical.
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Practical 4: Aim: Implement recursive best-first search algorithm for Romanian map problem.
Lesson 4 of 9 within section Practical. You must enroll in this course to access course content.
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Practical 5: Aim: Implement decision tree learning algorithm for the restaurant waiting problem.  
Lesson 5 of 9 within section Practical. You must enroll in this course to access course content.
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Practical 6: Aim: Implement feed forward back propagation neural network learning algorithm for the restaurant waiting problem.
Lesson 6 of 9 within section Practical. You must enroll in this course to access course content.
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Practical 7:  Aim: Implement Adaboost ensemble learning algorithm for the restaurant waiting problem.
Lesson 7 of 9 within section Practical. You must enroll in this course to access course content.
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Practical 8: Aim: Implement Naive Bayes’ learning algorithm for the restaurant waiting problem. 
Lesson 8 of 9 within section Practical. You must enroll in this course to access course content.
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Practical 9: Aim: Implement passive reinforcement learning algorithm based on adaptive dynamic programming (ADP) for the 3 by 4 world problem
Lesson 9 of 9 within section Practical. You must enroll in this course to access course content.
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