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Arti cial Intelligence Diploma ( 8 REVIEWS )
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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
25
NUMBER OF UNITS
0
NUMBER OF QUIZZES
18 HOURS, 44 MINUTES
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CURRICULUM
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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 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 https://edukite.org/course/artificial-intelligence-diploma/
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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
00:4 8:00
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
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Artificial Intelligence Diploma - Edukite
Lecture 14: Learning: Sparse Spaces, Phonology
00:4 8:00
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
00:4 8:00
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
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