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The Artificial Intelligence (AI) is a simulation of human intelligence process through machines such as computer systems. To help you understand artificial intelligence, you need to know first the basic knowledge representation of AI.

If you want to know more about AI then you are looking at the right [course_title]. Your knowledge on AI will be developed through knowing the intelligent systems by assembling and organizing solutions utilizing system engineering.


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 needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

  • Add the certificate to your CV or resume and brighten up your career
  • Show it to prove your success


Course Credit: MIT


Course Curriculum

Module: 1
Lecture 1: Introduction and Scope 00:47:00
Lecture 2: Reasoning: Goal Trees and Problem Solving 00:46:00
Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems 00:50:00
Lecture 4: Search: Depth-First, Hill Climbing, Beam 00:49:00
Lecture 5: Search: Optimal, Branch and Bound, A* 00:49:00
Lecture 6: Search: Games, Minimax, and Alpha-Beta 00:48:00
Module: 2
Lecture 7: Constraints: Interpreting Line Drawings 00:49:00
Lecture 8: Constraints: Search, Domain Reduction 00:45:00
Lecture 9: Constraints: Visual Object Recognition 00:52:00
Lecture 10: Introduction to Learning, Nearest Neighbors 00:50:00
Lecture 11: Learning: Identification Trees, Disorder 00:50:00
Lecture 12A: Neural Nets 00:51:00
Module: 3
Lecture 12B: Deep Neural Nets 00:49:00
Lecture 13: Learning: Genetic Algorithms 00:47:00
Lecture 14: Learning: Sparse Spaces, Phonology 00:48:00
Lecture 15: Learning: Near Misses, Felicity Conditions 00:47:00
Lecture 16: Learning: Support Vector Machines 00:50:00
Lecture 17: Learning: Boosting 00:52:00
Module: 4
Lecture 18: Representations: Classes, Trajectories, Transitions 00:49:00
Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind 00:49:00
Lecture 21: Probabilistic Inference I 00:48:00
Lecture 22: Probabilistic Inference II 00:49:00
Lecture 23: Model Merging, Cross-Modal Coupling, CourseSummary 00:50:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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