Intro to AI
survey of AI methods
Introduction to Artificial Intelligence will build practical understanding of what Artificial Intelligence is, the core mechanisms behind it, including probability, reasoning, and extracting decisions from data, and how these technologies are used in our world. By the end of the course, you will feel confident in your ability to apply AI principles to solve real-world problems and will have a sense of responsibility in your future AI-related endeavors. We will develop design and programming skills to build intelligent systems that can interact with the environment by learning and reasoning about the world. We will explore different methods for reasoning like informed search, probabilistic inference, uncertainty techniques, decision trees, and neural networks. This course provides a useful foundation for several courses involving intelligent systems, including (but not limited to) Machine Learning (CS4641), Knowledge-Based AI (CS4635), Computer Vision (CS4476), Robotics and Perception (CS3630), Natural Language Understanding (CS4650), and Game AI (CS4731)
Major topics addressed in this course:
- Search
- Markov Decision Processes
- Value/Policy Iteration
- Reinforcement Learning
- Q-learning
- Bayesian Networks
- Hidden Markov Models
- Particle Filters
- Machine Learning
- Neural Nets
- Ethics, Fairness, and Accoutability
Course Evaluation:
