Introduction to Artificial Intelligence (AI)

Introduction to Artificial Intelligence (AI)

Course Description:  

Artificial intelligence (AI) has become increasingly important in the modern world. Search engines, video games, financial algorithms, and autonomous vehicles all use AI to provide useful services. This course provides an introduction to the theory and approaches in artificial intelligence. A brief history of the origins of AI will be discussed, along with theory and practice of commonly used AI approaches. Sample topics discussed include search algorithms, logic algorithms, constraint satisfaction, and machine learning.

 

Course length: 5 Days

 

Course Design:

This 5 day instructor-led classroom course combines short lectures and classroom exercises to help students develop the knowledge and skills to function in the growing AI industry.

 

Instructor:

Dr. Alexander Stimpson

 

Target Audience:

Tech employees, consultants, engineers, IT personnel

 

Prerequisites:

Basic programming ability in Python

 

Objectives:

Upon completion of the course, students should be able to:

  • Identify the historical basis for modern AI
  • Craft simple AI algorithms for a variety of common AI problems
  • Understand the strengths and limitations of different AI approaches
  • Identify applications of AI in the modern world

 

Course Outline:

 

Day 1:

  • Course Introduction
  • Introduction to AI
  • History of AI
  • Search Algorithms
    • Tree search
    • Dynamic Programming
    • A*
  • Exercise: Search Algorithms

 

Day 2:

  • Markov Decision Processes (MDPs)
    • Policy evaluation and improvement
    • Policy/value iteration
    • Q-learning
  • Reinforcement Learning
  • Exercise: MDPs

 

Day 3:

  • Game Theory
  • Minimax, expectimax
  • Alpha-Beta pruning
  • Constraint Satisfaction
    • Factor Graphs
  • Exercise: Game Playing

 

Day 4:

  • Bayesian Networks
    • Bayesian inference
      • Gibbs sampling
    • Hidden Markov Models (HMMs)
    • Forward, Viterbi, Baum-welch algorithms
  • Logic
    • Syntax vs semantics
    • Propositional logic
    • First order logic
  • Exercise: HMM example

 

Day 5:

  • Machine Learning
    • Supervised Learning
      • Linear classification
      • Neural networks
      • K-nearest neighbors
    • Unsupervised Learning
      • K-means
    • Intro to Deep learning
  • Summary, future of AI
  • Exercise: K-NN example

 

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