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Instructor
Dr. Rebecca Wooten, Instructor - An Introduction to Machine Learning

Dr. Rebecca Wooten

Is an applied statistician and mathematician, well versed in various topics such as logic, set theory, combinatorics, probability, statistics, geometry, finance, game theory, and coding. She has written several books on statistical research and using technologies such as Excel and R. She posses 24 years worth of experience.
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Instructor: Dr. Rebecca Wooten

Inventory and Assets Management, Advertising Targeting and Product Pricing. Machine Learning Algorithms

  • Define Machine Learning and give a brief history of Machine Learning.
  • Define Data including Data Types, Data Dimensions, and Data Classifications in terms of Computer Programming and apply this knowledge in classifying their own data.
  • The instructor has applied statistics and mathematics experience spanning 24 years and is well-versed in topics such as logic, set theory, combinatorics, game theory, coding, and more. She holds a Ph.D. from University of South Florida.

Course Description

This mini course will explain what is machine learning and illustrates how machine learning is used in Inventory Management and Predictive Analysis; Assets Management and Risk; Informational Retrieval, Advertising Targeting, Product Pricing, and will explain machine learning algorithms.

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What am I going to get from this course?

  • Define Machine Learning and give a brief history of Machine Learning.
  • Define Data including Data Types, Data Dimensions, and Data Classifications in terms of Computer Programming and apply this knowledge in classifying their own data.
  • Define Algorithm including the various types of Algorithms and the Logic using in Computer Programming and apply these concepts to writing their own algorithms.
  • Understand what a Protocol is for, name the types of
  • Protocols used by computer networks, the different types of networks including the Internet and World Wide Web.
  • Understand the underlying numeral systems used in machine languages and the various levels of programming languages.
  • Understand how Machine Learning applies to Inventory Management, Assets Management and Risk Analysis and how Machine Learning applies to their business and personal finance.

Prerequisites and Target Audience

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

  • There is no required materials or software for this course.

Who should take this course? Who should not?

As an introduction to Machine Learning, this course is presented at a level that is readily understood by all individuals interested in Machine Learning. This course provides a history of Machine Learning, defines data and explains what is meant by big data; and classifies data in terms of computer programming. It covers the basic concept of numeral systems and the common numeral systems used by computer hardware to establish programming languages. Providing practical applications of Machine Learning.

Curriculum

Module 1: Introduction to Machine Learning

Lecture 1 An Introduction to Machine Learning

In this presentation we introduce the concept of computer data, algorithms, protocols, networks, the Internet and the World WIde Web; in addition to various computer langugaes, coding in terms of two numerical systems (Assemble Language).

Quiz 1 Quiz on An Introduction to Machine Learning

Quiz one covers several to the terms and concepts introduced in Lecture 1: An Introduction to Machine Learning.

Quiz 2 Quiz on An Introduction to Machine Learning (cont)

Quiz two covers several to the terms and concepts introduced in Lecture 1: An Introduction to Machine Learning.

Lecture 2 A brief history of Machine Learning

This presentaion covers a brief history of Machine Learning.

Quiz 3 Quiz on the History of Machine Learning

This quiz covers the topics introduced in the Brief History of Machine Learning.

Lecture 3 Datum and Data

This presentation outlines the various types and forms of computer data. How data is classified, introducting big data and the variable types used in machine learning.

Quiz 4 Quiz on Data Classifications: Declarations and Types

This quiz covers data dimensions and big data; in addition to data types.

Lecture 4 Algorithms

This presentation is on several types of algorithms and the logic used to instruct the computer.

Quiz 5 Quiz on Algorithms

This quiz covers the various types of algorithms.

Lecture 5 Protocols

In this presentation, we discuss the four main types of protocols in Machine Learning and introduce several protocols used in applicaitons.

Quiz 6 Quiz on Protocols

This quiz covers the various types of protocols.

Lecture 6 Networks
Quiz 7 Quiz on Types of Computer Networks

This quiz is on the various types of Computer Networks

Lecture 7 Logic Used in Programming

In this presentation, the logical statements and operators are introduced including conditional statements, loops and logical connective operators.

Quiz 8 Quiz on the Logic Used in Programming
Lecture 8 Numeral Systems

This presentation covers the common base ten and the two common computer bases: binary and hexadecimal.

Quiz 9 Quiz on Numeral Systems

This quiz cover the numeral systems used in computers and machine languages.

Module 2: Management using Machine Learning

Lecture 9 Inventory Management and Predictive Analysis

In this presentation, machine learning is applied to maintaining inventory and using predictive analysis to ensure that we are never left without inventory to sell.

Quiz 10 Inventory Management

This quiz covers inventory management including predictive analysis.

Lecture 10 Assets Management and Risk

This presentation is on assets management in machine learning

Quiz 11 Assets Management
Lecture 11 Information Retrieval and Advertising Targeting

In this presentation we discuss information retrieval in terms of personal information gathered by personal computers and data retrieval from a large number of files; in addition to has the results of Internet searches can result in target advertising.

Quiz 12 Information Retrieval and Advertising Targeting

This quiz covers the types of information retrieval and advertising Targeting.