Software Development
Exploring Artificial Intelligence
Adversarial Problems
Constraint Satisfaction Problems
Introducing Natural Language Processing
Introduction to Artificial Intelligence
Machine Learning
Reinforcement Learning
Search Problems
Uncertainty

Adversarial Problems

Course Number:
sd_exai_a04_it_enus
Lesson Objectives

Adversarial Problems

  • start the course
  • describe adversarial problems and the challenges they impose on AI
  • specify how to represent an adversarial problem
  • describe how to use the minimax algorithm to play an adversarial game and some of its shortcomings
  • describe how to use alpha-beta pruning to improve the performance of the minimax algorithm
  • describe evaluation functions
  • describe how to use cutoffs to be able to perform adversarial searches under a time constraint
  • describe how lookup tables can be used to improve an agent's performance
  • describe chess and how agents can be made to play the game of chess
  • describe expectiminimax values in stochastic games and how they make solution searching harder
  • describe different evaluation functions that can be used to search in a stochastic game
  • describe how to use monte carlo simulations to make decisions when searching
  • build a full high-level representation and solution for an adversarial game using the minimax algorithm and alpha-beta pruning

Overview/Description
Many problems occur in environments with more than one agent, such as games. In this course, you will learn some techniques used to solve adversarial problems to make agents play games, like chess.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Constraint Satisfaction Problems

Course Number:
sd_exai_a03_it_enus
Lesson Objectives

Constraint Satisfaction Problems

  • start the course
  • define constraint satisfaction problems and describe how they are different from search problems
  • list some examples of problems that are better for constraint satisfaction algorithms than search algorithms
  • describe how to use a backtracking search to solve a constraint satisfaction problem
  • describe how to order variables when performing a backtracking search
  • describe arc consistency and other types of constraint consistency in a constraint satisfaction problem
  • describe how to use arc consistency to solve a constraint satisfaction problem with constraint propagation
  • describe how to use the backjumping and forward checking inference method in a backtracking search
  • describe how local search algorithms can be used to solve constraint satisfaction problems
  • describe how to represent a Sudoku puzzle and how to solve it as a constraint satisfaction problem
  • build a full high-level representation and solution for a constraint satisfaction problem

Overview/Description
Search algorithms provide solutions for many problems, but they aren't always the optimal solution. This course will show you how constraint satisfaction algorithms are better than search algorithms in some cases, and how to use them.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Introducing Natural Language Processing

Course Number:
sd_exai_a08_it_enus
Lesson Objectives

Introducing Natural Language Processing

  • start the course
  • define NLP, and list some of its applications and methods
  • describe some of the base NLP operations such as Regex, tokenization, and stemming
  • describe the porter stemming algorithm used to stem English text
  • describe named entity recognition and some of the methods used to perform this task
  • describe how NLP models are built and the various parts required to build them
  • describe text classification, why it's useful, and how to perform it
  • describe the Naļve Bayes classification algorithm and how it can be used as a simple text classification algorithm
  • describe information retrieval and some of the base techniques used to perform this task
  • describe how an AI agent can use simple information retrieval techniques to answer some simple questions
  • describe parsing and how it can be accomplished using NLP
  • describe the challenges related to machine translation and some of the methods used to accomplish this task
  • describe methods used by computers to recognize speech
  • describe many different types of operations that can be used with natural language processing

Overview/Description
Natural language is essential to human communication, which makes the ability to process it an important one for computers. In this course, you will be introduced to natural language processing and some of the basic tasks.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Introduction to Artificial Intelligence

Course Number:
sd_exai_a01_it_enus
Lesson Objectives

Introduction to Artificial Intelligence

  • start the course
  • describe the four main definitions of artificial intelligence
  • describe some of the fields in artificial intelligence research and their applications
  • list some of the techniques used to build artificial intelligence systems
  • define intelligent agents
  • describe the different types of intelligent agents
  • define the task environment that intelligent agents live in
  • distinguish between an observable, a partially observable, and an unobservable environment, and describe how these affect agents
  • describe how the number of agents in a given environment can affect an agent
  • define deterministic and stochastic environments and how the level of certainty in an environment affects agents
  • describe the different types of environmental behavior and how this can affect agents
  • create a description of an environment related to a particular problem and how an agent might behave in that environment

Overview/Description
The world of artificial intelligence (AI) includes many areas in computing, which makes it a complex field. This course provides a useful description of AI, which will allow you to describe real-world problems as artificial environments.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Machine Learning

Course Number:
sd_exai_a06_it_enus
Lesson Objectives

Machine Learning

  • start the course
  • describe how AI learns and the different types of machine learning
  • describe how examples can be used for learning
  • describe decision trees and how the model expresses knowledge
  • describe entropy and information gain for learning decision tree models
  • describe how to choose attributes to learn a decision tree
  • describe overfitting and how decision tree models can be made to mitigate this issue
  • describe neural networks and how they apply to artificial intelligence
  • describe the structure of a neural network and its individual neurons
  • list some of the common types of neural networks and what problems they might be good at solving
  • describe how machine learning works with a perceptron
  • describe how perceptron learning can be generalized to a multilayered neural network
  • describe convolutional neural networks
  • describe recurrent neural networks
  • describe how a perceptron can learn how to achieve a particular result given a set of examples

Overview/Description
Sometimes agents must learn how to associate certain conditions with actions and outcomes. In this course, you will learn some of the principles of machine learning and how to use it to make smarter agents.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Reinforcement Learning

Course Number:
sd_exai_a07_it_enus
Lesson Objectives

Reinforcement Learning

  • start the course
  • describe reinforcement learning and list some of the techniques that agents can use to learn
  • describe additive rewards and discounted rewards
  • describe passive learning
  • describe how to use direct utility estimation for passive learning and how to define the Bellman Equation in the context of reinforced learning
  • describe temporal difference learning and contrast it with direct utility estimation
  • describe active learning and contrast it with passive learning
  • describe exploration and exploitation in the context of active reinforced learning and describe some of the exploration policies used in learning algorithms
  • define Q-learning for reinforced learning
  • describe the different parts used in Q-learning and how these can be implemented
  • describe on-policy and off-policy learning and the difference between the two
  • describe why lookup tables aren't ideal for most reinforced learning tasks and how to build some function approximations that can make these problems possible
  • describe how deep neural networks can be used to approximate q-value for given states in Q-learning
  • describe Q-learning and how to set up the algorithm for a particular problem

Overview/Description
Some problems are too complicated to describe to a computer and to solve with traditional algorithms, which is why reinforcement learning is useful. In this course, you will learn the fundamentals of reinforcement learning.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Search Problems

Course Number:
sd_exai_a02_it_enus
Lesson Objectives

Search Problems

  • start the course
  • define search problems and how these can be used by AI agents
  • list some problems that are ideal for searching algorithms
  • define how to represent search problems
  • describe the breadth-first search algorithm
  • describe the depth-first search algorithm
  • describe depth-limited search and the iterative deepening search algorithms
  • describe the greedy approach for best-first informed searching
  • define heuristics and their various properties
  • describe how to create a good heuristic function for a given search problem
  • describe the A* search algorithm
  • describe local searching and the hill-climbing search algorithm
  • describe the simulated annealing search algorithm and how it improves on hill-climbing search
  • describe the three environmental characteristicsof search problems, state the function for a consistent heuristic, and state the function for an A* search

Overview/Description
Many problems faced by intelligent agents can be solved using searching methods. This course will provide you with a definition for search problems and useful methods to solve these problems.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

Uncertainty

Course Number:
sd_exai_a05_it_enus
Lesson Objectives

Uncertainty

  • start the course
  • describe uncertainty and how it applies to AI
  • describe how probability theory is used to represent knowledge to help an intelligent make decisions
  • describe utility theory and how an agent can calculate expected utility of decisions
  • describe how preferences are involved in decision making and how the same problem can have different utility functions with different agents
  • describe how risks are taken into consideration when calculating utility and how attitude for risks can change the utility function
  • describe the utility of information gain and how information gain can influence decisions
  • define Markov chains
  • define the Markov Decision Process and how it applies to AI
  • describe the value iteration algorithm to decide on an optimal policy for a Markov Decision Process
  • define the partially observable Markov Decision Process and contrast it with a regular Markov Decision Process
  • describe how the value iteration algorithm is used with the partially observable Markov Decision Process
  • describe how a partially observable Markov Decision Process can be implemented with an intelligent agent
  • describe the Markov Decision Process and how it can be used by an intelligent agent

Overview/Description
Many problems aren't fully observable and have some degree of uncertainty, which is challenging for AI to solve. In this course, you will learn how to make agents deal with uncertainty and make the best decisions.

Target Audience
Anyone interested in artificial intelligence and how it can be used to solve many problems

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