Introduction to Module 5

Loading...

From the course by University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

4 ratings

Explore Related Videos

At Coursera, you will find the best lectures in the world. Here are some of our personalized recommendations for you

University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

4 ratings

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

From the lesson

Module 5: Fundamental Probabilistic Algorithms

This module starts by discussing practical machine learning workflows that are deployed in production environments, which emphasizes the big picture view of machine learning. Next this module introduces two additional fundamental algorithms: naive Bayes and Gaussian Processes. These algorithms both have foundations in probability theory but operate under very different assumptions. Naive Bayes is generally used for classification tasks, while Gaussian Processes are generally used for regression tasks. This module also discusses practical issues in constructing machine learning workflows.

Introduction to Module 53:34

Meet the Instructors

Robert Brunner
Professor
Accountancy

Hello.

We're now inside the Electrical and

Computer Engineering Building where we're surrounded by classrooms,

teaching labs, and office space.

During the spring semester,

the students in the College of Engineering hold an annual Engineering Open House.

Students form teams and build an engineering project to showcase what they have learned.

The atrium we're standing in is often filled with these showcases.

While some of these projects demonstrate basic engineering or physics phenomena,

for example, the non-Newtonian fluids are always popular.

In recent years, more projects have involved machine

learning either directly or indirectly,

as part of a hardware demonstration.

Given the nature of these projects,

the algorithms are often standard techniques

that are introduced in most machine learning classes.

This is true of our course as well.

And in this module, we introduce two additional fundamental algorithms,

Naive Bayes and Gaussian Processes.

Both of these techniques are rooted in probability theory and provide

a different view on using data to generate a predictive model.

Naive Bayes is an algorithm that is based on Bayes theorem,

hence the name, in which one computes

the probability of a given classification from a set of features.

Normally, one would need to worry about all of

the conditional probabilities between the different features.

However, the Naive part of the name refers to

the assumption that the occurrence of the different features are independent.

In most of the cases, this assumption is not strictly

valid if the algorithm often performs well,

especially in cases with large numbers of features such as text documents.

As a result, and because of the speed with which it can be calculated,

Naive Bayes is a fundamental algorithm that often serves as

a benchmark against which to compare more sophisticated techniques.

Gaussian Processes, on the other hand,

are used to build models to make continuous or regressive predictions from data.

In the Gaussian Process,

the data are assumed to be composed of random variables that are normally distributed.

The joint distribution of all of these Gaussian distributions forms the Gaussian Process.

This machine learning technique is a lazy learner,

like the k-nearest neighbor algorithm.

A prior for the data is specified,

and the data constrained the set of

possible Gaussian distributions to produce the predictive model.

This is a very versatile model and can generate probabilistic classifications,

making it powerful and efficient.

As the number of features becomes too large however,

say more than a few tens of features,

this technique can become inefficient.

Together with the nearest neighbor algorithm,

linear and logistic regression,

decision trees and support vector classifications,

these two algorithms form the fundamental set of machine learning techniques,

with which you should become familiar.

Knowing these algorithms, will enable you to tackle

most classification or regression tasks.

The readings and notebooks in this module will demonstrate how these algorithms work,

and how to apply them to different types of data. Good luck.

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.

Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.

© 2019 Coursera Inc. All rights reserved.

Download on the App Store

Get it on Google Play