Artificial Intelligence: Principles and Techniques

Artificial Intelligence (AI) applications are embedded in products and services in nearly every industry, from search engines, to speech recognition, medical devices, financial services, and even toys. In this course you will gain a broad understanding of the modern AI landscape.

You will learn how machines can engage in problem solving, reasoning, learning, and interaction, and you’ll apply your knowledge as you design, test, and implement new algorithms. You will gain the confidence and skills to analyze and solve new AI problems you encounter in your career.

• Get a solid understanding of foundational artificial intelligence principles and techniques, such as machine learning, state-based models, variable-based models, and logic.
• Implement search algorithms to find the shortest paths, plan robot motions, and perform machine translation.
• Find optimal policies in uncertain situations using Markov decision processes.
• Design agents and optimize strategies in adversarial games, such as Pac-Man.
• Adapt to preferences and limitations using constraint satisfaction problems (CSPs).
• Predict likelihoods of causes with Bayesian networks.
• Define logic in your algorithms with syntax, semantics, and inference rules.