Introduction to AI

Week 1

This class introduces the key concepts of AI:

  • The definition of AI, machine learning, and deep learning
  • Historical developments that now differentiate modern AI from prior AI
  • Examples of machine learning and deep learning
  • The differences between supervised and unsupervised learning
  • Examples of where AI is being applied


Week 2

This class covers the industries that are being transformed by AI and gives examples of:

  • Healthcare and genomics
  • Transportation and automated driving
  • Retail and supply chain
  • Finance
  • Industrial
  • Government


Week 3

This class focuses on AI in the enterprise, introduces the data science workflow, and teaches you how to:

  • Identify the steps in the data science workflow
  • Identify the key roles and skill sets within the field of AI
  • Describe ways to structure an AI team
  • Identify common data science misconceptions
  • Identify the components of AI model maintenance after deployment


Week 4

This class introduces the concept of supervised learning. You will be able to:

  • Explain how to formulate a supervised learning problem
  • Compare and understand the differences between training and inference
  • Describe the dangers of overfitting and training versus testing data
  • Understand how the Python programming language applies to AI

For a more advanced look at machine learning and supervised learning, see Machine Learning.


Week 5

This class focuses on data sources and types. As data is a critical part of training an artificial intelligence neural network, this lesson discusses:

  • How to recognize situations where more data samples are needed
  • Data wrangling, data augmentation, and feature engineering
  • How to identify problems like overfitting and underfitting
  • Several popular datasets used in training neural networks
  • Different data preprocessing methods
  • Ways to label data
  • How to identify challenges when working with data


Week 6

This session reviews the principles of deep learning, including:

  • The basics of deep learning and how it fits within AI and machine learning
  • The types of problems that deep learning resolves
  • The steps in building a neural network model
  • The definition of a convolutional neural network (CNN)
  • Transfer learning and why it's useful
  • Common deep learning architectures

For a more advanced look at deep learning, see Deep Learning.


Week 7

This week covers hardware, including:

  • End-to-end computing for AI
  • The capabilities provided by data centers, gateways, and edge computing
  • The different processor types from data center to edge
  • How Intel® hardware applies to AI


Week 8

Conclude the course with a review of the important software building blocks. This class covers:

  • Deep learning frameworks
  • Intel® architecture-optimized libraries and frameworks
  • The impact of big data and the use of the BigDL library for Apache Spark*
  • Getting access to the Intel® AI DevCloud