Artificial Intelligence v7.0.0

Nanodegree key: nd898

Version: 7.0.0

Locale: en-us

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

Content

Part 01 : 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.

Module 01: Introduction to Artificial Intelligence
- Lesson 01: Welcome to Artificial Intelligence
  Welcome to Introduction to Artificial Intelligence!
- Lesson 02: Introduction to Artificial Intelligence
  An introduction to basic AI concepts and the challenge of answering "what is AI?"
- Lesson 03: Solving Sudoku With AI
  In this lesson, you'll dive right in and apply Artificial Intelligence to solve every Sudoku puzzle.
- Lesson 04: 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 05: 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.
  
  Project Description - Build a Sudoku Solver
  
  Project Rubric - Build a Sudoku Solver
  - Concept 01: Project Intro
  - Concept 02: Project Workspace
- Lesson 06: 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.

Part 02 : 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.

Module 01: Classical Search
- Lesson 01: 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 02: 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 03: 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 04: Classroom Exercise: Search
  Complete a practice exercise where you'll implement informed and uninformed search strategies for the game PacMan.
- Lesson 05: Additional Search Topics
  References to additional readings on search.

Part 03 : 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.

Module 01: Automated Planning
- Lesson 01: 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 02: 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 03: Classical Planning
  Peter presents a survey of Classical Planning techniques: forward planning (progression search) & backward planning (regression search).
- Lesson 04: 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.
  
  Project Description - Build a Forward-Planning Agent
  
  Project Rubric - Build a Forward-Planning Agent
- Lesson 05: Additional Planning Topics
  Peter discusses plan space search & situational calculus. Finish the lesson with readings on advanced planning topics & modern applications of automated planning.

Part 04 : (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.

Module 01: Optimization Problems
- Lesson 01: 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 02: Hill Climbing
  Thad introduces Hill Climbing, a very simple local search optimization technique that works well on many iterative improvement problems.
- Lesson 03: Simulated Annealing
  Thad explains Simulated Annealing, a classical global optimization technique for optimization.
- Lesson 04: Genetic Algorithms
  Thad introduces another optimization technique: Genetic Algorithms, which uses a population of samples to make iterative improvements towards the goal.
- Lesson 05: Optimization Exercise
  Complete a classroom exercise implementing simulated annealing to solve the traveling salesman problem.
  - Concept 01: Local Search Exercise
  - Concept 02: Coding Exercise: Local Search
- Lesson 06: 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.
  - Concept 01: Similarities Between Optimizers
  - Concept 02: Reading List

Part 05 : Adversarial Search

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 human.

Module 01: Adversarial Search
- Lesson 01: 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.
  - Concept 01: Course Introduction
  - Concept 02: Course Prerequisites
- Lesson 02: 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.
- Lesson 03: Optimizing Minimax Search
  Some of the limitations of minimax search and introduces optimizations & changes that make it practical in more complex domains.
- Lesson 04: Build an Adversarial Game Playing Agent
  Build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.
  
  Project Description - Build an Adversarial Game Playing Agent
  
  Project Rubric - Build an Adversarial Game Playing Agent
  - Concept 01: Project Introduction
  - Concept 02: Workspace
- Lesson 05: Extending Minimax Search
  Extensions to minimax search to support more than two players and non-deterministic domains.
- Lesson 06: 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.

Part 06 : Fundamentals of Probabilistic Graphical Models

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.

Module 01: Probabilistic Models
- Lesson 01: 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.
- Lesson 02: Probability
  Sebastian Thrun briefly reviews basic probability theory including discrete distributions, independence, joint probabilities, and conditional distributions to model uncertainty in the real world.
- Lesson 03: Spam Classifier with Naive Bayes
  In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.
- Lesson 04: Bayes Nets
  Sebastian explains using Bayes Nets as a compact graphical model to encode probability distributions for efficient analysis.
- Lesson 05: Inference in Bayes Nets
  Sebastian explains probabilistic inference using Bayes Nets, i.e. how to use evidence to calculate probabilities from the network.
- Lesson 06: 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.
- Lesson 07: Dynamic Time Warping
  Thad explains the Dynamic Time Warping technique for working with time-series data.
- Lesson 08: Project: Part of Speech Tagging
  In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.
  
  Project Description - Part of Speech Tagging
  
  Project Rubric - Part of Speech Tagging
  - Concept 01: Introduction
  - Concept 02: Workspace: Part of Speech Tagging

Part 07 : After the AI Nanodegree Program

Once you've completed the last project, review the information here to discover resources for you to continue learning and practicing AI.

Module 01: After the AI Nanodegree Program
- Lesson 01: Additional Topics in AI
  Suggested resources to continue learning about artificial intelligence after completing the Nanodegree program.
  - Concept 01: Additional Topics in AI

**Part 08 (Elective):** Extracurricular

Additional lecture material on hidden Markov models and applications for gesture recognition.

Module 01: Extracurricular
- Lesson 01: Hidden Markov Models
  Thad returns to discuss using Hidden Markov Models for pattern recognition with sequential data.
- Lesson 02: 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.

**Part 09 (Career):** Career Services

Module 01: Career Services
- Lesson 01: 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.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile
- Lesson 02: 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.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile