MLE2: Introduction to Statistical Learning Theory
Statistical learning theory is an important element of understanding the multi-layered complexities inherent in machine learning. It is a framework that builds on decision theory, statistics, functional analysis, and more. In this course, instructor Asha Saxena explores statistical learning theory and its application within machine learning, especially in regard to predictive modeling, continuous vs. categorical data, error typology, approaches to errors, direct business applications, and statistical notation, among many other elements.
Statistical learning theory is an important element of understanding the multi-layered complexities inherent in machine learning. It is a framework that builds on decision theory, statistics, functional analysis, and more. In this course, instructor Asha Saxena explores statistical learning theory and its application within machine learning, especially in regard to predictive modeling, continuous vs. categorical data, error typology, approaches to errors, direct business applications, and statistical notation, among many other elements.
Purpose
To explore and understand the importance and practical application of statistical learning theory as it relates to machine learning.
Outcome
- Understand the fundamentals of statistical learning theory
- Explore various approaches and examples
- Discuss predictive modeling
This Course Includes:
- A 43-minute educational training video
- A 16-question exam
- Self-paced and on-demand e-learning
- Unlimited course access
- Course CEUs: 1
Individual Course Price: $99
Learning Plan Price: $499
Learning Plan CEUs: 6
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