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
|What is Artificial Intelligence (AI)?||01:00:00|
|Problem Solving and Search||01:50:00|
|Satisfiability and Validity||02:00:00|
|Resolution Theorem Proving; Propositional Logic||01:25:00|
|Resolution Theorem Proving; First Order Logic||01:05:00|
|Partial-Order Planning Algorithms||01:24:00|
|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|
|Submit Your Assignment||00:00:00|
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