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Artificial intelligence (AI) is the branch of computer science that emphasises the creation of intelligent machines that work and reacts like humans. Simply, it is the intelligence demonstrated by machines. The [course_title] course illustrates the basic concepts of artificial intelligence covering representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques. At first, you learn the definition and meaning of AI that will be followed by the basic supervised learning methods, and Bayesian network inference and learning with Hidden Variables, decision making under Uncertainty, Markov Decision Processes, and Reinforcement Learning.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT 


Course Curriculum

What is Artificial Intelligence (AI)? 01:00:00
Problem Solving and Search 01:50:00
Logic 00:58:00
Satisfiability and Validity 02:00:00
First-Order Logic 01:25:00
Resolution Theorem Proving; Propositional Logic 01:25:00
Resolution Theorem Proving; First Order Logic 01:05:00
Logic Miscellanea 00:20:00
Planning 01:25:00
Partial-Order Planning Algorithms 01:24:00
Graph Plan 01:38:00
Planning Miscellany 01:08:00
Probability 01:27:00
Bayesian Networks 01:38:00
Inference in Bayesian Networks 01:18:00
Where do Bayesian Networks Come From? 01:26:00
Learning With Hidden Variables 01:30:00
Decision Making under Uncertainty 01:45:00
Markov Decision Processes 01:14:00
Reinforcement Learning 00:35:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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