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Instructor
Dr. Stephen Huff, Instructor - Supervised Learning: Linear Regression

Dr. Stephen Huff

Is currently employed as a consulting scientist, advising DHS Science and Technology programs regarding machine learning technology. He earned his Ph.D. (Bioinformatics) from the University of Houston. In addition to that, he has 3 Masters and a B.S.: an M.S. (Joint Professional Military Education) from Air Command and Staff College, M.S. (Management Information Systems) from Wright State University, M.S. (Biology) from University of Houston at Clear Lake, and a B.S. (Microbiology with Chemistry Minor) from University of Texas at Arlington.

Instructor: Dr. Stephen Huff

    • Acquire improved ability to discriminate, differentiate, and conceptualize appropriate methods of supervised machine learning methods.
    • Instructor has a Ph.D. in Bioinformatics and works as a consultant for the DHS Agency.

Course Description

What am I going to get from this course?

  • Improved ability to discriminate, differentiate, and conceptualize appropriate methods of supervised machine learning methods
  • Improved general awareness regarding use of these models as L1-/L2-norm regularizers, loss-functions, and more.  

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Basic understanding of machine learning principles
  • Basic awareness of linear regression methods
  • Basic general understanding of information technology principles
  • Optional prerequisites: Basic working knowledge of Python and Keras, additional awareness of at least one Keras backend (TensorFlow, Microsoft Cognitive, ToolKit and/or Theano)

Who should take this course? Who should not?

  • Information Technology professionals with basic knowledge of machine learning theory and linear regression

Curriculum

Module 1: Course Introduction

Lecture 1 Promo
Lecture 2 Course Introduction
Lecture 3 Course Overview
Lecture 4 Bias Error
Lecture 5 Lessons Summary

Module 2: Ordinary Least Squares Linear Regression

Lecture 6 Lesson Overview
Lecture 7 Ordinary Least Squares

Module 3: Ridge Regression

Lecture 8 Lesson Overview
Lecture 9 Ridge Trace
Lecture 10 LASSO Regression
Lecture 11 Use Cases in Security

Module 4: Elastic Net Regression

Lecture 12 Lesson Overview
Lecture 13 Use Cases in Temporal Analyses

Module 5: Non-Parametric Kernel-Based Regression

Lecture 14 Lesson Overview
Lecture 15 Kernel Regression
Lecture 16 Gaussian Process

Module 6: Application in Healthcare, Education, Financial Services, Retail, & Travel Enterprise

Lecture 17 Lesson Overview & Various Applications in Different Industries

Module 7: Code Base

Lecture 18 Lesson Overview
Lecture 19 Activation Functions in Keras
Lecture 20 Course Conclusion
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