Exploring Artificial Intelligence

Gain an understanding of AI, learn how to describe real world problems as artificial environments, and about the various concepts and techniques in artificial intelligence.

Access Time: 1 Month

Course Details:

Introduction to Artificial Intelligence

Get an understanding of AI, which will allow you to describe real-world problems as artificial environments.

Topics Covered

  • 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

Search Problems

A definition for search problems and useful methods to solve these problems

Topics Covered

  • Introducing Search Problems
  • Brute Force Searching
  • Informed Searching
  • Local Searching
  • Practice: Identifying Search Problems

Constraint Satisfaction Problems

See how constraint satisfaction algorithms are better than search algorithms in some cases, and how to use them

Topics Covered

  • 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

Adversarial Problems

Learn some techniques used to solve adversarial problems to make agents play games, like chess

Topics Covered

  • 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 perfor

  • ance 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


Learn how to make agents deal with uncertainty and make the best decisions

Topics Covered

  • Understanding Uncertainty
  • Understanding Utility Theory
  • Examining the Markov Decision Process
  • Practice: Markov Decision Process

Machine Learning

Learn some of the principles of machine learning and how to use it to make smarter agents

Topics Covered

  • describe how AI learns and the different types of machine learning
  • describe how examples can be used for learning
  • Decision Trees
  • Neural Networks
  • Practice: Perceptron Training

Reinforcement Learning

Learn the fundamentals of reinforcement learning

Topics Covered

  • 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

Introducing Natural Language Processing

Get introduced to natural language processing and some of the basic tasks

Topics Covered

  • Defining NLP
  • Basic Models
  • Communication
  • Practice: NLP Operations

Course Fee: USD 75

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