1. Home
  2. Program
  3. Preconference Training
  4. Fundamentals of AI
SEE OTHER DATES AND OPTIONS See Pricing & Packages
Sunday, June 2, 2024 - 8:30am to Monday, June 3, 2024 - 5:00pm

Fundamentals of AI

Unravel the complexities of machine learning in a way that's approachable for everyone, regardless of technical background. In Getting Started with Machine Learning, participants embark on a journey through the world of machine learning, understanding not only the what but the why and how behind this transformative technology.

Rather than diving deep into complex algorithms, this course takes a practical approach to machine learning. Participants engage in hands-on activities and discussions that showcase the real-world applications of machine learning in various industries including healthcare, finance, recommendation systems, and more.

Key takeaways from this class include:

  • Breaking down the notion of machine learning into simple, relatable terms, offering a glimpse into how machines learn from data.
  • Understanding the types of machine learning and their impact on everyday life.
  • Demystifying machine learning algorithms by explaining their functions using familiar examples.
  • Understanding critical ethical considerations like biases, fairness, and the responsible use of AI.

By the end of this course, participants emerge equipped with a clear understanding of the fundamental principles of machine learning, enabling them to engage in informed discussions and make sense of the pervasive role of this technology in our rapidly evolving world.

Who Should Attend
This class is for anyone, technical or non-technical, who wants to understand the applicability of machine learning to everyday life. It is also for anyone who wants to learn the basics of machine learning and how to interact with Generative AI tools such as ChatGPT or Windows Copilot.

Course Outline

Session 1: Introduction to Machine Learning

  • Definitions of AI, ML, LLM, and deep learning
  • Importance and applications of machine learning
  • Overview of the machine learning workflow
  • Model interpretation and explainability
  • Ethics and bias in machine learning
  • Environment setup for hands-on exercise
  • Exercise #1: AI Fact or Fiction?

Session 2: Understanding the Machine Learning Process

  • Overall development process
  • Data engineering
  • Model selection, training, and validation
  • Testing and deployment
  • Operation, monitoring, retraining
  • MLOps
  • Exercise #2: Quiz on ML process

Session 3: Types of Learning

  • Types of machine learning (supervised, unsupervised, reinforcement)
  • What is supervised learning?
  • Classification vs. Regression
  • Decision trees and random forest examples
  • Model evaluation and metrics (accuracy, precision, recall, F1-score)
  • Exercise #3: Real-world example - classifying images
  • What is unsupervised learning?
  • Clustering algorithms (K-Means, Hierarchical, DBSCAN) examples
  • Dimensionality reduction (PCA, t-SNE)
  • Exercise #4: Real-world example - using clustering
  • What is reinforcement learning?
  • Reinforcement learning approaches
  • Exercise #5: Real-world example - Using reinforcement learning

Session 4: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • How these networks work
  • Feedforward and back propagation
  • Deep learning and large language models (LLM)
  • Key deep learning architectures (FNNs, CNNs, RNNs, GANs)
  • Transformer architectures
  • Fine-tuning existing models
  • Exercise #6: Leveraging pre-existing models

Session 5: Generative AI and Prompt Engineering

  • What is Generative AI?
  • Application of Generative AI
  • Understanding prompt engineering
  • Using prompt engineering techniques
  • Prompt engineering best practices
  • Demonstration of Google Gemini
  • Demonstration of GitHub Copilot
  • Exercise #7: Creating good prompts with ChatGPT

Exercise #8: Putting it all together

 

Wrap up & Next steps

  • Recap of key concepts and skills learned
  • Course evaluation
  • Resources for further learning (books, online courses, communities)
  • Thank you!

Class Daily Schedule

Sign-In/Registration 7:30 - 8:30 a.m.
Morning Session 8:30 a.m. - 12:00 p.m.
Lunch 12:00 - 1:00 p.m.
Afternoon Session 1:00 - 5:00 p.m.
Times represent the typical daily schedule. Please confirm your schedule at registration.

Training Course Fee Includes

• Digital course materials
• Continental breakfasts and refreshment breaks
• Lunches

Jeff Payne

Jeffery Payne

Coveros

Jeffery Payne is CEO and founder of Coveros, Inc., a company that helps organizations accelerate software delivery using agile methods. Prior to founding Coveros, he was the co-founder of application security company Cigital, where he served as CEO for 16 years.

Jeffery is a recognized software expert and popular keynote speaker at both business and technology conferences on a variety of software quality, security, DevOps, and agile topics. He has testified in front of congress on issues such as digital rights mgmt., software quality, and software research.

Jeffery is the technical editor of the AgileConnection community (www.agileconnection.com).