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. See More: https://bit.ly/2DLINVT

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Arti cial Intelligence Diploma
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The Arti cial Intelligence (AI) is a simulation of human intelligence process through machines such
as computer systems. To help you …
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LEVEL 3 - DIPLOMA
COURSE BADGE
COURSE CERTIFICATE
25NUMBER OF UNITS
0NUMBER OF QUIZZES
18 HOURS, 44 MINUTES
HOME CURRICULUM REVIEWS
The Arti cial Intelligence (AI) is a simulation of human intelligence process through machines such
as computer systems. To help you understand arti cial intelligence, you need to know rst the basic
knowledge representation of AI.
If you want to know more about AI then you are looking at the right Arti cial Intelligence Diploma.
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 Certi cate at a cost of £49.
Having an Of cial Edukite Certi cation is a great way to celebrate and share your success. You can:
Add the certi cate to your CV or resume and brighten up your career
Show it to prove your success
Course Credit: MIT

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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: Identi cation 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
COURSE CURRICULUM

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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
Certi cation 00:00:00
COURSE REVIEWS
4.9

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