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Introduction to Artificial Intelligence

Course

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.

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.

Advanced

4 weeks

Real-world Projects

Completion Certificate

Last Updated January 25, 2023

Skills you'll learn:
Constraint propagation • Constraint satisfaction problems • Search algorithms • Backtracking search
Prerequisites:
Object-oriented programming basics • Command line interface basics • Basic algorithms

Course Lessons

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.

Taught By The Best

Photo of Arpan Chakraborty

Arpan Chakraborty

Instructor

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

Photo of David Joyner

David Joyner

Instructor

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.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

  • Gain proven experience

  • Retain knowledge longer

  • Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.

  • Get help from subject matter experts

  • Learn industry best practices

  • Gain valuable insights and improve your skills