Overview:
Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
About the Course:
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. You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.
Audience:
The target audience for this course includes students and professionals who are interested in learning robotics and biometrics.
Pre-Requisite:
- Understanding of the fundamentals of Python programming
- Basic knowledge of statistics
- Basic machine learning knowledge
Course Curriculum
| Course Outline | |||
| Introduction to AI, history of AI, course logistics Details | 00:00:00 | ||
| Intelligent agents, uninformed search Details | 00:00:00 | ||
| Heuristic search, A* algorithm Details | 00:00:00 | ||
| Adversarial search, games Details | 00:00:00 | ||
| Constraint Satisfaction Problems Details | 00:00:00 | ||
| Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors Details | 00:00:00 | ||
| Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning Details | 00:00:00 | ||
| Markov decision processes and reinforcement learning Details | 00:00:00 | ||
| Logical Agent, propositional logic and first order logic Details | 00:00:00 | ||
| AI applications (Vision/Robotics) Details | 00:00:00 | ||
| Review and Conclusion Details | 00:00:00 | ||
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