Course

Course Summary
Credit Type:
Course
ACE ID:
EDX-0004
Organization:
Location:
Online
Length:
7 weeks (140 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Introduction to Artificial Intelligence with Python
Description

Objective:

The course objective is to explore the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Learning Outcomes:

  • Describe the foundation of artificial intelligence
  • Develop/programmatic search solutions
  • Apply propositional logic and knowledge engineering to AI solutions
  • Use probability, sampling and the application of Markov Models to program AI decision making
  • Apply algorithms to choose the best option programmatically
  • Apply machine learning concepts such as supervised learning, nearest-neighbor classification and perceptron learning to contribute to AI application
  • Use neural networks algorithmic structures in python for AI application
  • Design programmatic solutions for natural language processing

General Topics:

  • Graph search algorithms
  • Adversarial search
  • Knowledge representation
  • Logical inference
  • Probability theory
  • Bayesian networks
  • Markov models
  • Constraint satisfaction
  • Machine learning
  • Reinforcement learning
  • Neural networks: Natural language processing
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Computer Based Training
  • Lectures
  • Practical Exercises
  • Project-based Instruction

Methods of Assessment:

  • Other
  • Quizzes
  • Working code projects

Minimum Passing Score:

70%
Supplemental Materials