Artificial Intelligence Training for Beginners

Starting June 3

Mondays & Wednesdays

6:30 - 8:30 PM US Pacific Time



Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science.

Course Schedule

About this course

  • The duration of the course is 16 hours.
  • What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?
  • They are all complex real world problems being solved with applications of intelligence (AI).
  • This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

What you will learn in this course?

  • In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

What are the pre-requisites?

  • No prerequisite is required.
  • Some statistics, probability, computer background will be helpful

Course Outline

  1. Introduction, course logistics. History of AI, what is AI and what it can do.
  2. The use of AI in life and business, AI Agents and domain knowledge representation
  3. Deterministic AI environments, information classification, clustering and normalization
  4. Stochastic AI environments, randomness and probabilistic reasoning
  5. AI result processing and comparisons, distance metrics, algorithm training, OCR sample
  6. Introduction to AI algorithms, scoring, error calculation
  7. Uninformed and heuristic search, A* algorithm and graph traversal, adversarial search
  8. Constraint Satisfaction Problems
  9. Machine Learning: basic concepts, linear models, K nearest neighbors, neural networks
  10. Decision theory, features and relationships, computational sustainability.
  11. Conclusion