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Artificial Intelligence and Machine Learning Basics for Non-Technical Professionals

Class Sessions

Date Location Cost
  • Thu Sep 19, 9:00 am to 4:00 pm
Birch Hall 107 (Birch Lab B) $400

Class Code

ITS-1918

Class Description

This course provides a fun and non-technical introduction to Artificial Intelligence and Machine Learning. It provides the vocabulary and basics for this exciting new world.

Prerequisite: Basic programming knowledge preferred

This Artificial Intelligence (AI) and Machine Learning (ML) class helps in awareness about AI and ML patterns and use cases in real world. You will get an understanding of ML concepts like Supervised and Unsupervised learning techniques and usages. We will discuss the difference between AI vs ML vs Deep Learning (DL) along with usage patterns. We will help you expand your vocabulary in AI to understand techniques like Classification, Clustering and Regression. Finally, we would do a ML demo to illustrate few tools and next steps.

 

In this course, you will have an opportunity to learn how to:

  • Describe Supervised and Unsupervised learning techniques and usages
  • Compare AI vs ML vs DL
  • Understand techniques like Classification, Clustering and Regression
  • Discuss how to identify which kinds of technique to be applied for specific use case
  • Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
  • Understand the relation between Data Engineering and Data Science
  • Understand the Data Science process
  • Discuss Machine Learning use cases in different domains
  • Identify when to use or not use Machine Learning
  • Define how to form a ML team for success
  • Understand usage of tools through a ML Demo and hands-on labs.

 

Topic Outline:

  • Course Introduction
  • History and background of AI and ML
  • Compare AI vs ML vs DL
  • Describe Supervised and Unsupervised learning techniques and usages
  • Machine Learning patterns
    - Classification
    - Clustering
    - Regression
  • Gartner Hype Cycle for Emerging Technologies
  • Machine Learning offerings in Industry
  • Discuss Machine Learning use cases in different domains
  • Understand the Data Science process to apply to ML use cases
  • Understand the relation between Data Engineering and Data Science
  • Identify the different roles needed for successful ML project
  • Hands-on: Create account for Microsoft Azure Machine Learning Studio
  • Demo: ML using Azure ML studio
  • Demo: ML using Scikit-learn
  • References and Next steps


     

 

University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.