Become an expert in the core concepts of artificial intelligence and learn how to apply them to real-life problems.
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
- 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
- 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.
- (Optional) Optimization Problems
- 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. Finish the lesson by completing a classroom exercise comparing the different algorithms' performance on a variety of problems.
- Adversarial Search
- 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 human.
- Fundamentals of Probabilistic Graphical Models
- 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.
- After the AI Nanodegree Program
- Once you've completed the last project, review the information here to discover resources for you to continue learning and practicing AI.
- Extracurricular
- Additional lecture material on hidden Markov models and applications for gesture recognition.
- Career Services
Taught by
Peter Norvig, Sebastian Thrun, Thad Starner, Peter K., Eduardo R., Ming R., Weipeng S., shashank rao m. and Rama Krishna J.
