Artificial Intelligence Nanodegree
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Artificial Intelligence Nanodegree

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udacity公众号

Nanodegree key: nd898
Version: 6.0.0
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

Meet the instructional team including Sebastian Thrun, Peter Norvig, and Thad Starner who will be teaching you about the foundations of AI. Get acquainted with the resources available in your classroom & other important information about the program. Complete the lesson by building a Sudoku solver.

Module 01: Introduction to the Nanodegree
- Lesson 01: Welcome to Artificial IntelligenceWelcome to the Artificial Intelligence Nanodegree program!
- Lesson 02: Knowledge, Community, and CareersYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 03: Get Help with Your AccountWhat to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 04: Intro to Artificial IntelligenceAn introduction to basic AI concepts and the challenge of answering “what is AI?”
- Lesson 05: Solving Sudoku With AIIn this lesson, you’ll dive right in and apply Artificial Intelligence to solve every Sudoku puzzle.
- Lesson 06: WorkspacesReview the basic functionality of Workspaces—pre-configured development environments in the Udacity classroom for projects and exercises.
- Lesson 07: Setting Up Your Environment with AnacondaIf 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 08: Build a Sudoku SolverUse 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 SolverProject Rubric - Build a Sudoku Solver
  - Concept 01: Project Intro
  - Concept 02: Project Workspace
Module 02: Career Services
- Lesson 01: Jobs in AILearn about common jobs in artificial intelligence, and get tips on how to stay active in the community.
  - Concept 01: Jobs in AI
  - Concept 02: Access the Career Portal
- Lesson 02: Optimize Your GitHub ProfileOther 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 ProfileProject Rubric - Optimize Your GitHub Profile
  - Concept 01: Prove Your Skills With GitHub
  - Concept 02: Introduction
  - Concept 03: GitHub profile important items
  - Concept 04: Good GitHub repository
  - Concept 05: Interview with Art - Part 1
  - Concept 06: Identify fixes for example “bad” profile
  - Concept 07: Quick Fixes #1
  - Concept 08: Quick Fixes #2
  - Concept 09: Writing READMEs with Walter
  - Concept 10: Interview with Art - Part 2
  - Concept 11: Commit messages best practices
  - Concept 12: Reflect on your commit messages
  - Concept 13: Participating in open source projects
  - Concept 14: Interview with Art - Part 3
  - Concept 15: Participating in open source projects 2
  - Concept 16: Starring interesting repositories
  - Concept 17: Next Steps
    Part 02 : Constraint Satisfaction Problems
    Take a deep dive into the constraint satisfaction problem framework and further explore constraint propagation, backtracking search, and other CSP techniques. Complete a classroom exercise using a powerful CSP solver on a variety of problems to gain experience framing new problems as CSPs.
Module 01: Constraint Satisfaction Problems
- Lesson 01: Constraint Satisfaction ProblemsExpand 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.
- Lesson 02: CSP Coding ExercisePractice formulating some classical example problems as CSPs, and then to explore using a powerful open source constraint satisfaction tool called Z3 from Microsoft Research to solve them.
  - Concept 01: Introduction
  - Concept 02: Coding Exercise: Using CSPs
Module 02: Additional Constraint Problem Topics
- Lesson 01: Additional ReadingsReading list of applications and additional topics related to CSPs.
  - Concept 01: Reading List
    Part 03 : 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: Introduction
- Lesson 01: IntroductionPeter Norvig, co-author of Artificial Intelligence: A Modern Approach, explains a framework for search problems, and introduces uninformed & informed search strategies to solve them.
Module 02: Uninformed Search
- Lesson 01: Uninformed SearchPeter introduces uninformed search strategies—which can only solve problems by generating successor states and distinguishing between goal and non-goal states.
Module 03: Informed Search
- Lesson 01: Informed SearchPeter introduces informed search strategies, which means that they use problem-specific knowledge to find solutions more efficiently than uninformed search.
Module 04: Classroom Exercise: Search
- Lesson 01: Classroom Exercise: SearchComplete a practice exercise where you’ll implement informed and uninformed search strategies for the game PacMan.
  - Concept 01: Exercise Overview
  - Concept 02: (Coding) Workspace: Pacman Search
Module 05: Additional Search Topics
- Lesson 01: Additional Search TopicsReferences to additional readings on search.
  - Concept 01: Problems with Search
  - Concept 02: Peter’s take on AI
  - Concept 03: Suggested Readings
    Part 04 : 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: Symbolic Logic & Reasoning
- Lesson 01: Symbolic Logic & ReasoningPeter 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.
Module 02: Automated Planning
- Lesson 01: Introduction to PlanningPeter 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 02: Classical PlanningPeter presents a survey of Classical Planning techniques: forward planning (progression search) & backward planning (regression search).
Module 03: Build a Forward Planning Agent
- Lesson 01: Build a Forward-Planning AgentIn 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 AgentProject Rubric - Build a Forward-Planning Agent
  - Concept 01: Project Intro
  - Concept 02: Workspace
Module 04: Additional Planning Topics
- Lesson 01: Additional Planning TopicsPeter discusses plan space search & situational calculus. Finish the lesson with readings on advanced planning topics & modern applications of automated planning.
  - Concept 01: Plan Space Search
  - Concept 02: Sliding Puzzle Example
  - Concept 03: Situation Calculus 1
  - Concept 04: Situation Calculus 2
  - Concept 05: Situation Calculus 3
  - Concept 06: Automated Planning References
    Part 05 : 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: IntroductionThad 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.
Module 02: Local Search
- Lesson 01: Hill ClimbingThad introduces Hill Climbing, a very simple local search optimization technique that works well on many iterative improvement problems.
- Lesson 02: Simulated AnnealingThad explains Simulated Annealing, a classical global optimization technique for optimization.
- Lesson 03: Genetic AlgorithmsThad introduces another optimization technique: Genetic Algorithms, which uses a population of samples to make iterative improvements towards the goal.
Module 03: Optimization Exercise
- Lesson 01: Optimization ExerciseComplete a classroom exercise implementing simulated annealing to solve the traveling salesman problem.
  - Concept 01: Local Search Exercise
  - Concept 02: Coding Exercise: Local Search
Module 04: Additional Optimization Topics
- Lesson 01: Additional Optimization TopicsReview 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 06 : 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: Game Playing
- Lesson 01: Search in Multiagent DomainsThad returns to teach search in multi-agent domains, using the Minimax theorem to solve adversarial problems and build agents that make better decisions than humans.
- Lesson 02: Optimizing Minimax SearchThad explains some of the limitations of minimax search and introduces optimizations & changes that make it practical in more complex domains.
Module 02: Build an Adversarial Search Agent
- Lesson 01: Build an Adversarial Game Playing AgentExtend classical search to adversarial domains, to build agents that make good decisions without any human intervention—such as the DeepMind AlphaGo agent.Project Description - Build an Adversarial Game Playing AgentProject Rubric - Build an Adversarial Game Playing Agent
  - Concept 01: Project Introduction
  - Concept 02: Workspace
Module 03: Additional Topics in Adversarial Search
- Lesson 01: Extending Minimax SearchThad introduces extensions to minimax search to support more than two players and non-deterministic domains.
- Lesson 02: Additional Adversarial Search TopicsIntroduce Monte Carlo Tree Search, a highly-successful search technique in game domains, along with a reading list for other advanced adversarial search topics.
Module 04: Career Services
- Lesson 01: Take 30 Min to Improve your LinkedInFind 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 ProfileProject Rubric - Improve Your LinkedIn Profile
  - Concept 01: Get Opportunities with LinkedIn
  - Concept 02: Use Your Story to Stand Out
  - Concept 03: Why Use an Elevator Pitch
  - Concept 04: Create Your Elevator Pitch
  - Concept 05: Use Your Elevator Pitch on LinkedIn
  - Concept 06: Create Your Profile With SEO In Mind
  - Concept 07: Profile Essentials
  - Concept 08: Work Experiences & Accomplishments
  - Concept 09: Build and Strengthen Your Network
  - Concept 10: Reaching Out on LinkedIn
  - Concept 11: Boost Your Visibility
  - Concept 12: Up Next
    Part 07 : Probabilistic 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: Probability Refresher
- Lesson 01: ProbabilitySebastian Thrun briefly reviews basic probability theory including discrete distributions, independence, joint probabilities, and conditional distributions to model uncertainty in the real world.
Module 02: Naive Bayes
- Lesson 01: Naive BayesIn this section, you’ll learn how to build a spam e-mail classifier using the naive Bayes algorithm.
Module 03: Bayes Networks
- Lesson 01: Bayes NetsSebastian explains using Bayes Nets as a compact graphical model to encode probability distributions for efficient analysis.
- Lesson 02: Inference in Bayes NetsSebastian explains probabilistic inference using Bayes Nets, i.e. how to use evidence to calculate probabilities from the network.
Module 04: Hidden Markov Models
- Lesson 01: Hidden Markov ModelsLearn Hidden Markov Models, and apply them to part-of-speech tagging, a very popular problem in Natural Language Processing.
Module 05: Project: Part of Speech Tagging
- Lesson 01: Part of Speech TaggingIn this project you will build a hidden Markov model (HMM) to perform part of speech tagging, a common pre-processing step in Natural Language Processing.Project Description - Part of Speech TaggingProject Rubric - Part of Speech Tagging
  - Concept 01: Introduction
  - Concept 02: Workspace: Part of Speech Tagging
Module 06: Additional Topics in PGMs
- Lesson 01: Dynamic Time WarpingThad explains the Dynamic Time Warping technique for working with time-series data.
- Lesson 02: Additional Topics in PGMsReading list of select topics to continue learning about probabilistic graphical models.
  - Concept 01: Reading List
    Part 08 : 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: Additional Topics in AI
- Lesson 01: Additional Topics in AISuggested resources to continue learning about artificial intelligence after completing the Nanodegree program.
  - Concept 01: Additional Topics in AI
    Part 09 (Elective): Extracurricular
    Additional lecture material on hidden Markov models and applications for gesture recognition.
Module 01: Hidden Markov Models
- Lesson 01: Hidden Markov ModelsThad returns to discuss using Hidden Markov Models for pattern recognition with sequential data.
- Lesson 02: Advanced HMMsThad shares advanced techniques that can improve performance of HMMs recognizing American Sign Language, and more complex HMM models for applications like speech synthesis.

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人工智能系列-2022

AI人工智能 6.0

Artificial Intelligence Nanodegree

Content

Part 01 : Introduction to Artificial Intelligence

Part 02 : Constraint Satisfaction Problems

Part 03 : Classical Search

Part 04 : Automated Planning

Part 05 : Optimization Problems

Part 06 : Adversarial Search

Part 07 : Probabilistic Models

Part 08 : After the AI Nanodegree Program

**Part 09 (Elective):** Extracurricular

AI人工智能 6.0

Artificial Intelligence Nanodegree

Content

Part 01 : Introduction to Artificial Intelligence

Part 02 : Constraint Satisfaction Problems

Part 03 : Classical Search

Part 04 : Automated Planning

Part 05 : Optimization Problems

Part 06 : Adversarial Search

Part 07 : Probabilistic Models

Part 08 : After the AI Nanodegree Program

Part 09 (Elective): Extracurricular

**Part 09 (Elective):** Extracurricular