About Course
Artificial Intelligence: Principles and Techniques
Do you want to learn more about Artificial Intelligence? Do you want to learn how to create the most powerful AI model ever created, as well as play against it? Sounds appealing, doesn’t it?
This course covers the following topics:
What are AI, Machine Learning, and Deep Learning, and how do they differ from business intelligence?
Seven AI Journey Principles
How do you know if a problem requires AI at all?
How to become data ready if AI is required – Tuscane Approach
How can you identify if a piece of software employs AI, and if so, what kind of AI it uses?
How do you tell the difference between a strong AI solution and a weak one?
How to figure out which AI technique(s) can address your company’s or organization’s specific problem
How do I get others in my organization to utilize it?
How to properly deploy it without wasting thousands of dollars and hours of work
How can you figure out how much an AI solution is worth?
Best Practices for Formulating an Artificial Intelligence Strategy
7 Human-AI Work Policy Framing Principles
How can an individual and an organization reduce the hazards linked with AI?
How AI analyses various sorts of data, produces predictions, recognizes photos, connects with customers, or teaches a robot to behave like a person! This will entail some of the most widely used Machine Learning and Deep Learning techniques today: Clustering, Association Rules, Search Algorithm, Ensemble Learning, Classification, Regression, Decision Trees, Ensemble Learning,
Who this course is For
Executive leaders, managers, and administrators will benefit from this course.
University / College Students who have recently graduated
Course Content
Artificial Intelligence: Principles and Techniques
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Lecture 2: Machine Learning 1
00:00 -
Lecture 3: Machine Learning 2
00:00 -
Lecture 4: Machine Learning 3
00:00 -
Lecture 5: Search 1 – Dynamic Programming, Uniform Cost Search
00:00 -
Lecture 6: Search 2 – A*
00:00 -
Lecture 7: Markov Decision Processes – Value Iteration
00:00 -
Lecture 8: Markov Decision Processes – Reinforcement Learning
00:00 -
Lecture 9: Game Playing 1 – Minimax, Alpha-beta Pruning
00:00 -
Lecture 10: Game Playing 2 – TD Learning, Game Theory
00:00 -
Lecture 11: Factor Graphs 1 – Constraint Satisfaction Problems |
00:00 -
Lecture 12: Factor Graphs 2 – Conditional Independence
00:00 -
Lecture 13: Bayesian Networks 1 – Inference
00:00 -
Lecture 14: Bayesian Networks 2 – Forward-Backward
01:11:49 -
Lecture 15: Bayesian Networks 3 – Maximum Likelihood
01:23:45 -
Lecture 16: Logic 1 – Propositional Logic
00:00 -
Lecture 17: Logic 2 – First-order Logic
00:00 -
Lecture 18: Deep Learning
00:00 -
Lecture 19: Conclusion
00:00