Artificial Intelligence: Principles and Techniques

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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

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What Will You Learn?

  • Continued access to the most recent publications on cutting-edge AI advances
  • Seven principles for leading an organization's AI journey
  • Machine Learning and Deep Learning Techniques
  • How to evaluate AI effectiveness and lead AI adoption in your company
  • How to tell the difference between good and bad AI solutions and get the results you want
  • How should AI policy and strategy be framed?
  • How to Use AI to Manage Organizational Risks

Course Content

Artificial Intelligence: Principles and Techniques

  • Lecture 2: Machine Learning 1
  • Lecture 3: Machine Learning 2
  • Lecture 4: Machine Learning 3
  • Lecture 5: Search 1 – Dynamic Programming, Uniform Cost Search
  • Lecture 6: Search 2 – A*
  • Lecture 7: Markov Decision Processes – Value Iteration
  • Lecture 8: Markov Decision Processes – Reinforcement Learning
  • Lecture 9: Game Playing 1 – Minimax, Alpha-beta Pruning
  • Lecture 10: Game Playing 2 – TD Learning, Game Theory
  • Lecture 11: Factor Graphs 1 – Constraint Satisfaction Problems |
  • Lecture 12: Factor Graphs 2 – Conditional Independence
  • Lecture 13: Bayesian Networks 1 – Inference
  • Lecture 14: Bayesian Networks 2 – Forward-Backward
  • Lecture 15: Bayesian Networks 3 – Maximum Likelihood
  • Lecture 16: Logic 1 – Propositional Logic
  • Lecture 17: Logic 2 – First-order Logic
  • Lecture 18: Deep Learning
  • Lecture 19: Conclusion

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