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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.
Assessment
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.
Certification
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 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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