Artificial Intelligence Nanodegree

Nanodegree key: nd898

Version: 5.0.0

Locale: en-us

Become an expert in the core concepts of artificial intelligence and learn how to apply them to real-life problems.


Part 01 : Introduction to Artificial Intelligence

Meet the instructional team including Sebastian Thrun, Peter Norvig, and Thad Starner who will be teaching you about the foundations of AI. Get acquainted with the resources available in your classroom & other important information about the program. Complete the lesson by building a Sudoku solver.

Part 02 : Constraint Satisfaction Problems

Take a deep dive into the constraint satisfaction problem framework and further explore constraint propagation, backtracking search, and other CSP techniques. Complete a classroom exercise using a powerful CSP solver on a variety of problems to gain experience framing new problems as CSPs.

Part 03 : 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.

Part 04 : 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.

Part 05 : (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.

Part 06 : 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.

Part 07 : Probabilistic 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.

Part 08 : 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.

Part 09 (Elective): Extracurricular

Additional lecture material on hidden Markov models and applications for gesture recognition.