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Expand Your Knowledge of Artificial Intelligence

Nanodegree Program

Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more.

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  • Estimated time
    3 Months

    At 12-15 hrs/week

  • Enroll by
    December 7, 2022

    Get access to classroom immediately on enrollment

  • Prerequisites
    Algebra, Calculus, Statistics, & Python

What you will learn

  1. Learn Foundational AI Algorithms

    3 months to complete

    Learn to write programs using the foundational AI algorithms powering everything from NASA’s Mars Rover to DeepMind’s AlphaGo Zero. This program will teach you classical AI algorithms applied to common problem types. You’ll master Bayes Networks and Hidden Markov Models, and more.

    Prerequisite knowledge

    1. 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.

    2. 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.

      All our programs include:

      • Real-world projects from industry experts

        With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.

      • Technical mentor support

        Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.

      • Career services

        You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.

      • Flexible learning program

        Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

      Program offerings

      • Class content

        • Real-world projects
        • Project reviews
        • Project feedback from experienced reviewers
      • Student services

        • Technical mentor support
        • Student community
      • Career services

        • Github review
        • Linkedin profile optimization

      Succeed with personalized services.

      We provide services customized for your needs at every step of your learning journey to ensure your success.

      Get timely feedback on your projects.

      • Personalized feedback
      • Unlimited submissions and feedback loops
      • Practical tips and industry best practices
      • Additional suggested resources to improve
      • 1,400+

        project reviewers

      • 2.7M

        projects reviewed

      • 88/100

        reviewer rating

      • 1.1 hours

        avg project review turnaround time

      Learn with the best.

      Learn with the best.

      • Peter Norvig

        Research Director, Google

        Peter Norvig is a Director of Research at Google and is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field.

      • Sebastian Thrun


        As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.

      • Thad Starner

        Professor of Computer Science, Georgia Tech

        Thad Starner is the director of the Contextual Computing Group (CCG) at Georgia Tech and is also the longest-serving Technical Lead/Manager on Google's Glass project.

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