Learn from the world’s foremost AI experts, and develop a deep understanding of algorithms being applied to real-world problems in natural language processing, computer vision, bioinformatics, and more. Practice a structured approach for applying these techniques to new challenges, and emerge fully prepared to advance in the field.
Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more.
Overview
Syllabus
- Introduction to Artificial Intelligence
- In this course, you'll learn about the foundations of AI. You'll configure your programming environment to work on AI problems with Python. At the end of the course you'll build a Sudoku solver and solve constraint satisfaction problems.
- Classical Search
- In this course you’ll learn classical graph search algorithms--including uninformed search techniques like breadth-first and depth-first search and informed search with heuristics including A*. These algorithms are at the heart of many classical AI techniques, and have been used for planning, optimization, problem solving, and more. Complete the lesson by teaching PacMan to search with these techniques to solve increasingly complex domains.
- Automated Planning
- In this course you’ll learn to represent general problem domains with symbolic logic and use search to find optimal plans for achieving your agent’s goals. Planning & scheduling systems power modern automation & logistics operations, and aerospace applications like the Hubble telescope & NASA Mars rovers.
- Optimization Problems
- In this course you’ll learn about iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes, and have been used extensively in fields like Operations Research & logistics. You’ll finish the lesson by completing a classroom exercise comparing the different algorithms' performance on a variety of problems.
- Adversarial Search
- In this course you’ll learn how to search in multi-agent environments (including decision making in competitive environments) using the minimax theorem from game theory. Then build an agent that can play games better than any humans.
- Fundamentals of Probabilistic Graphical Models
- In this course you’ll learn to use Bayes Nets to represent complex probability distributions, and algorithms for sampling from those distributions. Then learn the algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition. HMMs have been used for gesture recognition in computer vision, gene sequence identification in bioinformatics, speech generation & part of speech tagging in natural language processing, and more.
Taught by
Peter Norvig, Sebastian Thrun, Thad Starner, Peter K., Eduardo R., Ming R., Weipeng S., shashank rao m. and Rama Krishna J.
