Description
Learn to code using the fundamental AI algorithms that power everything from NASA's Mars Rover to DeepMind's AlphaGo Zero. You'll learn Beam Search and Random Hill Climbing, as well as Bayes Networks and Hidden Markov Models.
Syllabus:
Course 1: Intro to Artificial Intelligence
LESSON ONE Welcome to the Program
- Meet the course instructors and Udacity team. Learn about the resources available to help you succeed
LESSON TWO Intro to Artificial Intelligence
- Consider the meaning of “artificial intelligence”
- Be able to define core concepts from AI including “agents”, “environments”, and “states”
- Learn the concept of “rational” behavior for AI agents LESSON THREE Setting Up your Environment with Anaconda
- Install the software and complete necessary system configuration you’ll need for the projects
Course 2: Constraint Satisfaction Problems
LESSON ONE Solving Sudoku With AI
- Express logical constraints as Python functions
- Use constraint propagation & search to solve all Sudoku puzzles
LESSON TWO Constraint Satisfaction Problems
- Learn to represent problems in terms of logical constraints
- Use constraint propagation to limit the potential solution space
- Incorporate backtracking search to find a solution when the set of constraints is incomplete
LESSON THREE Additional Topics in CSP
- List of external resources for you to continue learning about CSPs
Course Project: Build a Sudoku Solver
Reason is used by humans to solve problems by breaking down the problem statement and incorporating domain knowledge to narrow the possible solution space. In this project, you will use constraint propagation and backtracking search to create an agent that only considers reasonable solution candidates and efficiently solves any Sudoku puzzle. This approach can be found in many classic AI problems, and the solution techniques have been extended and applied to a wide range of problems in bioinformatics, logistics, and operations research.
In this project, you will demonstrate some basic algorithm knowledge and learn to solve general problems using constraint satisfaction.
Course 3: Search, Optimization, and Planning
LESSON ONE Introduction
- Learn about the significance of search in AI
LESSON TWO Uninformed Search
- Learn uninformed search techniques including depth-first order, breadth-first order, and Uniform Cost Search
LESSON THREE Informed Search
- Learn informed search techniques (using heuristics) including A*
- Understand admissibility and consistency conditions for heuristics
LESSON FOUR Additional Topics: Search
- List of external resources for you to continue learning about search
LESSON FIVE Classroom Exercise: Search
- Implement informed & uninformed search for Pacman
LESSON SIX Introduction: Optimization Problems
- Introduce iterative improvement problems that can be solved with optimization
LESSON SEVEN Hill Climbing
- Learn Random Hill Climbing for local search optimization problems
LESSON EIGHT Simulated Annealing
- Learn to use Simulated Annealing for global optimization problems
LESSON NINE Genetic Algorithms
- Explore and implement Genetic Algorithms that keep a pool of candidates to solve optimization problems
LESSON TEN Additional Optimization Topics
- Learn about improvements & optimizations to optimization search including Late Acceptance Hill Climbing, Basin Hopping, & Differential Evolution
LESSON ELEVEN Classroom Exercise: Optimization Problems
- Compare optimization techniques on a variety of problems
LESSON TWELVE Symbolic Logic & Reasoning
- Learn Propositional logic (propositions & statements)
- Learn First-Order logic (quantifiers, variables, & objects)
- Encode problems with symbolic constraints using first- order logic
LESSON THIRTEEN Introduction to Automated Planning
- Learn to define planning problems
LESSON FOURTEEN Classical Planning
- Learn high-level features of automated planning techniques using search & symbolic logic including forward planning, backwards planning, & hierarchical planning
- Explore planning heuristics & planning graphs
LESSON FIFTEEN Additional Topics in Planning
- List of external resources for you to continue learning about planning
Course Project: Build a Forward Planning Agent
Intelligent agents are expected to act in complex domains where their goals and objectives may be difficult to achieve in the short term.
They must think about their goals and make rational decisions about how to achieve them. In this project, you will create a system that uses symbolic logic to represent general problem domains and classical search to find optimal plans for your agent's goals. Modern automation and logistics operations, as well as aerospace applications such as the Hubble telescope and NASA Mars rovers, rely on planning and scheduling systems.
You will demonstrate your understanding of classical optimization and search algorithms, symbolic logic, and domain-independent planning in this project.
Course 4: Adversarial Search
LESSON ONE Search in MultiAgent Domains
- Understand “adversarial” problems & applications (e.g., multi-agent environments)
- Extend state space search techniques to domains your agents do not fully control
- Learn the minimax search technique
LESSON TWO Optimizing Minimax Search
- Learn techniques used to overcome limitations in basic minimax search like depth-limiting and alpha-beta pruning,
LESSON THREE Extending Minimax Search
- Extend adversarial search to non-deterministic domains and domains with more than two players
LESSON FOUR Additional Adversarial Search Topics
- List of external resources for you to continue learning about adversarial search
Course Project: Build an Adversarial Game Playing Agent
Intelligent agents are expected to act in complex domains where their goals and objectives may be difficult to achieve in the short term.
They must think about their goals and make rational decisions about how to achieve them. In this project, you will create a system that uses symbolic logic to represent general problem domains and classical search to find optimal plans for your agent's goals. Modern automation and logistics operations, as well as aerospace applications such as the Hubble telescope and NASA Mars rovers, rely on planning and scheduling systems.
You will demonstrate your understanding of classical optimization and search algorithms, symbolic logic, and domain-independent planning in this project.
Course 5: Fundamentals of Probabilistic Graphical Models
LESSON ONE Probability
- Review key concepts in probability including discrete distributions, joint probabilities, and conditional probabilities
LESSON TWO Bayes Networks
- Efficiently encode joint probabilities in Bayes networks
LESSON THREE Inference in Bayes Nets
- Learn about inference in Bayes networks through exact enumeration with optimizations
- Learn techniques for approximate inference in more complex Bayes networks
LESSON FOUR Hidden Markov Models
- Learn parameters to maximize the likelihood of model parameters to training data
- Determine the likelihood of observing test data given a fixed model
- Learn an algorithm to Identify the most likely sequence of states in a model given some data
LESSON FIVE Dynamic Time Warping
- Learn the dynamic time warping algorithm for time-series analysis
LESSON SIX Additional Topics in PGMs
- List of external resources for you to continue learning about probabilistic graphical models
Course Project: Part of Speech Tagging
By explicitly modelling your agents' belief state as a distribution over all possible states, probabilistic models enable your agents to better deal with the uncertainty of the real world. Part of speech tagging, a common pre-processing step in Natural Language Processing, will be performed using a Hidden Markov Model (HMM) in this project. HMMs have found widespread application in NLP, speech recognition, bioinformatics, and computer vision tasks.