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Artificial Intelligence

Nanodegree Program

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

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

Advanced

3 months

Real-world Projects

Completion Certificate

Last Updated June 18, 2024

Skills you'll learn:
Optimization algorithms • Likelihood function • Minimax search • Bayesian networks
Prerequisites:
Object-oriented Python • Intermediate Python • Object-oriented programming basics

Courses In This Program

Course 1 4 weeks

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.

Lesson 1

Welcome to Artificial Intelligence

Welcome to Introduction to Artificial Intelligence!

Lesson 2

Introduction to Artificial Intelligence

An introduction to basic AI concepts and the challenge of answering "what is AI?"

Lesson 3

Solving Sudoku With AI

In this lesson, you'll dive right in and apply Artificial Intelligence to solve every Sudoku puzzle.

Lesson 4

Setting Up Your Environment and Workspaces

If you do not want to use Workspaces, then follow these instructions to set up your own system using Anaconda, a popular tool to manage your environments and packages in python.

Lesson 5 • Project

Build a Sudoku Solver

Use constraint propagation and search to build an agent that reasons like a human would to efficiently solve any Sudoku puzzle.

Lesson 6

Constraint Satisfaction Problems

Expand from the constraint propagation technique used in the Sudoku project to the Constraint Satisfaction Problem framework that can be used to solve a wide range of general problems.

Course 2 4 hours

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.

Lesson 1

Introduction

Peter Norvig, co-author of _Artificial Intelligence: A Modern Approach_, explains a framework for search problems, and introduces uninformed & informed search strategies to solve them.

Lesson 2

Uninformed Search

Peter introduces uninformed search strategies—which can only solve problems by generating successor states and distinguishing between goal and non-goal states.

Lesson 3

Informed Search

Peter introduces informed search strategies, which means that they use problem-specific knowledge to find solutions more efficiently than an uninformed search.

Lesson 4

Classroom Exercise: Search

Complete a practice exercise where you'll implement informed and uninformed search strategies for the game PacMan.

Lesson 5

Additional Search Topics

References to additional readings on search.

Course 3 3 weeks

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.

Lesson 1

Symbolic Logic & Reasoning

Peter Norvig returns to explain propositional logic and first-order logic, which provide a symbolic logic framework that enables AI agents to reason about their actions.

Lesson 2

Introduction to Planning

Peter Norvig defines automated planning problems in comparison to more general problem solving techniques to set the stage for classical planning algorithms in the next lesson.

Lesson 3

Classical Planning

Peter presents a survey of Classical Planning techniques: forward planning (progression search) & backward planning (regression search).

Lesson 4 • Project

Build a Forward-Planning Agent

In this project you’ll use experiment with search and symbolic logic to build an agent that automatically develops and executes plans to achieve their goals.

Lesson 5

Additional Planning Topics

Peter discusses plan space search & situational calculus. Finish the lesson with readings on advanced planning topics & modern applications of automated planning.

Course 4 4 hours

(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.

Lesson 1

Introduction

Thad Starner introduces the concept of _iterative improvement problems_, a class of optimization problems that can be solved with global optimization or local search techniques covered in this lesson.

Lesson 2

Hill Climbing

Thad introduces _Hill Climbing_, a very simple local search optimization technique that works well on many iterative improvement problems.

Lesson 3

Simulated Annealing

Thad explains _Simulated Annealing_, a classical global optimization technique for optimization.

Lesson 4

Genetic Algorithms

Thad introduces another optimization technique: _Genetic Algorithms_, which uses a population of samples to make iterative improvements towards the goal.

Lesson 5

Optimization Exercise

Complete a classroom exercise implementing simulated annealing to solve the traveling salesman problem.

Lesson 6

Additional Optimization Topics

Review similarities of the techniques introduced in this lesson with links to readings on advanced optimization topics, then complete an optimization exercise in the classroom.

Taught By The Best

Photo of Sebastian Thrun

Sebastian Thrun

Founder and Executive Chairman, Udacity

As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.

Photo of Thad Starner

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.

Photo of Peter Norvig

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.

Ratings & Reviews

Average Rating: 4.5 Stars

162 Reviews

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About Artificial Intelligence

Our Artificial Intelligence Nanodegree program is a comprehensive artificial intelligence course designed for advanced learners. In this program, you'll explore optimization algorithms, likelihood functions, minimax search, Bayesian networks, foundational AI concepts and apply them to solve real-life problems. The program covers classical search algorithms, automated planning, and optimization problems, providing a diverse range of AI skills. Taught by esteemed experts like Sebastian Thrun, Thad Starner, and Peter Norvig, this course offers an unparalleled opportunity to learn from the best in the field. At Udacity, we are committed to providing practical, real-world learning experiences. Our project-based approach ensures that you apply your skills to actual scenarios, gaining hands-on experience and immediate applicability. With our top-tier services, including personalized project reviews and access to industry experts, you are set up for success. Enroll in our AI program to gain cutting-edge skills and join a community of forward-thinking learners.

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