Nanodegree Course

Artificial Intelligence - Search and Optimization

by Georgia Institute of Technology

Learn classical techniques for efficiently solving search and optimization problems.

Enroll In Nanodegree

About this Course

This course is a part of the Artificial Intelligence Nanodegree Program.

Skill Level
Beginner
Included in Course
  • Rich Learning Content

  • Interactive Quizzes

  • Taught by Industry Pros

  • Real World Projects

  • Student Support Community

  • Personalized Career Support

Join the Path to Greatness

This course is part of a Nanodegree Program. It is a step towards a new career in Artificial Intelligence.

Nanodegree Course

Artificial Intelligence - Search and Optimization

by Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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What You Will Learn

Lesson 1

Introduction to Game Playing

  • In this lesson, you'll learn about how to build a Game Playing AI Agent. You'll focus on an agent that wins in the board game Isolation! You'll learn some of the seminal techniques in AI Game Playing including Adversarial Search and Minimax Trees.
Lesson 1

Introduction to Game Playing

  • In this lesson, you'll learn about how to build a Game Playing AI Agent. You'll focus on an agent that wins in the board game Isolation! You'll learn some of the seminal techniques in AI Game Playing including Adversarial Search and Minimax Trees.
Lesson 2

Advanced Game Playing

  • In this lesson, you'll build a Game-Playing agent that defeats opponents in Isolation. Along the way, you'll learn about advanced Game-Playing techniques such as Iterative Deepening, Alpha-Beta Pruning, and Expectimax.
Lesson 2

Advanced Game Playing

  • In this lesson, you'll build a Game-Playing agent that defeats opponents in Isolation. Along the way, you'll learn about advanced Game-Playing techniques such as Iterative Deepening, Alpha-Beta Pruning, and Expectimax.
Lesson 3

Search

  • In this lesson, you'll learn how to implement some of the seminal search algorithms that are a cornerstone of AI including Breadth-First Search, Depth-First Search, and finally A Star Search. You'll then put your skills to the test by teaching Pac-Man to navigate his world and complete complex tasks such as finding the fastest path through the map while maximizing points scored.
Lesson 3

Search

  • In this lesson, you'll learn how to implement some of the seminal search algorithms that are a cornerstone of AI including Breadth-First Search, Depth-First Search, and finally A Star Search. You'll then put your skills to the test by teaching Pac-Man to navigate his world and complete complex tasks such as finding the fastest path through the map while maximizing points scored.
Lesson 4

Simulated Annealing

  • In this lesson, you'll learn how to explore spaces and avoid local optima by using Simulated Annealing. In the process, you'll solve the famous n-Queens problem using this advanced AI technique!
Lesson 4

Simulated Annealing

  • In this lesson, you'll learn how to explore spaces and avoid local optima by using Simulated Annealing. In the process, you'll solve the famous n-Queens problem using this advanced AI technique!
Lesson 5

Constraint Satisfaction

  • In this lesson we'll return to one of the main techniques we used to solve Sudoku - constraint propagation. We'll see how to use known constraints to solve a wide variety of problems including Map Coloring problems, and simple puzzles.
Lesson 5

Constraint Satisfaction

  • In this lesson we'll return to one of the main techniques we used to solve Sudoku - constraint propagation. We'll see how to use known constraints to solve a wide variety of problems including Map Coloring problems, and simple puzzles.

Prerequisites and Requirements

Graph Theory, Set Theory, Data Structures, and Python

See the Technology Requirements for using Udacity.

Why Take This Course

In this AI Search and Optimization course, you will learn about Uninformed Search (DFS, BFS, UCS), Informed search (A*), Adversarial search (minimax, alpha-beta pruning), Random optimization (hill-climbing, simulated annealing) and Constraint Satisfaction Problems.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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