Artificial Intelligence

Welcome to Artificial Intelligence

Welcome to Introduction to Artificial Intelligence!

Introduction to Artificial Intelligence

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

Solving Sudoku With AI

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

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.

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.

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.

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.

Uninformed Search

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

Informed Search

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

Classroom Exercise: Search

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

Additional Search Topics

References to additional readings on search.

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.

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.

Classical Planning

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

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.

Additional Planning Topics

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

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.

Hill Climbing

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

Simulated Annealing

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

Genetic Algorithms

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

Optimization Exercise

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

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.

Introduction to Adversarial Search

Extend classical search to adversarial domains, to build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.

Search in Multiagent Domains

Search in multi-agent domains, using the Minimax theorem to solve adversarial problems and build agents that make better decisions than humans.

Optimizing Minimax Search

Some of the limitations of minimax search and introduces optimizations & changes that make it practical in more complex domains.

Build an Adversarial Game Playing Agent

Build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.

Extending Minimax Search

Extensions to minimax search to support more than two players and non-deterministic domains.

Additional Adversarial Search Topics

Introduce Monte Carlo Tree Search, a highly-successful search technique in game domains, along with a reading list for other advanced adversarial search topics.

Introduction to Probabilistic Models

Welcome to Fundamentals of Probabilistic Graphical Models. In this lesson, we will cover the course overview, prerequisites, and do a brief introduction to probability.

Probability

Sebastian Thrun briefly reviews basic probability theory including discrete distributions, independence, joint probabilities, and conditional distributions to model uncertainty in the real world.

Spam Classifier with Naive Bayes

In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.

Bayes Nets

Sebastian explains using Bayes Nets as a compact graphical model to encode probability distributions for efficient analysis.

Inference in Bayes Nets

Sebastian explains probabilistic inference using Bayes Nets, i.e. how to use evidence to calculate probabilities from the network.

Part of Speech Tagging with HMMs

Learn Hidden Markov Models, and apply them to part-of-speech tagging, a very popular problem in Natural Language Processing.

Dynamic Time Warping

Thad explains the Dynamic Time Warping technique for working with time-series data.

Project: Part of Speech Tagging

In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.

Additional Topics in AI

Suggested resources to continue learning about artificial intelligence after completing the Nanodegree program.

Hidden Markov Models

Thad returns to discuss using Hidden Markov Models for pattern recognition with sequential data.

Advanced HMMs

Thad shares advanced techniques that can improve performance of HMMs recognizing American Sign Language, and more complex HMM models for applications like speech synthesis.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.