Introduction to Module 2

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From the course by University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

3 ratings

University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

3 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 2: Fundamental Algorithms

This module introduces several of the most important machine learning algorithms: logistic regression, decision trees, and support vector machine. Of these three algorithms, the first, logistic regression, is a classification algorithm (despite its name). The other two, however, can be used for either classification or regression tasks. Thus, this module will dive deeper into the concept of machine classification, where algorithms learn from existing, labeled data to classify new, unseen data into specific categories; and, the concept of machine regression, where algorithms learn a model from data to make predictions for new, unseen data. While these algorithms all differ in their mathematical underpinnings, they are often used for classifying numerical, text, and image data or performing regression in a variety of domains. This module will also review different techniques for quantifying the performance of a classification and regression algorithms and how to deal with imbalanced training data.

Introduction to Module 25:39

Meet the Instructors

Robert Brunner
Professor
Accountancy

Hello. I am standing outside the new electrical and computer engineering building,

at the University of Illinois.

This building opened in the fall of 2014 and

provides space for both high tech learning and collaboration.

The building has approximately 230,000 square feet of space,

that is devoted to classrooms,

offices and laboratory collaboration.

This building is designed to be one of the largest net zero energy buildings

in the country and has many fundamental features to enable this goal;

Including solar rays on the roof,

a Terra cotta exterior and sunshades.

This module introduces several fundamental machine learning algorithms.

Thus, it is appropriate to be outside the ECE building,

which is a marvel of modern engineering and where faculty and

students develop new hardware and software approaches to improve machine learning.

These basic algorithms are important to learn for several reasons.

First; Learning these basic techniques

will help you strengthen your data analytics intuition,

which will guide you in future analysis projects.

Second; Even if a more powerful machine learning technique is used in the end,

these fundamental algorithms are often quick and easy to run and they

thus provide useful benchmarks against which

other more complex techniques can be compared.

Third; More powerful algorithms are often based on these fundamental techniques.

Thus, learning these algorithms is often

the first step in learning more advanced techniques.

This module will begin by exploring

examples of machine learning being used in business and accountancy.

Next, you will learn about several fundamental algorithms.

The first three of which are logistic regression,

decision trees and support vector machines.

The first of these algorithms,

logistic regression is despite its name,

actually an algorithm for performing classification.

This technique employs a linear equation to

predict which of two classes an instance belongs.

As a result, this technique is popular since

the linear equation makes this model easy to understand and explain.

The actual prediction is based on the logic function which

maps the entire real numbers into the range zero to one.

Thus we have a mapping between the result of

the linear equation and the probability of being Class one or Class two.

This lesson will also introduce different classification metrics,

including the concepts of true positive,

true negative, false positive and false negative.

Many popular performance metrics are based on these four values,

including things such as; precision,

recall, sensitivity, and type one and type two errors.

The second algorithm; decision tree,

recursively subdivides a data set by splitting along different features one at a time.

This process starts with a single group node that represents the entire set of data.

As the data are subdivided into two child populations,

new nodes are constructed.

This process continues constructing

an inverted tree eventually stopping with a set of leaf nodes.

A leaf node is created either when a predefined stopping criteria is reached,

for example the maximum tree depth or when all instances are roughly equivalent.

When splitting a node, the feature is chosen to optimally split the data.

This feature and the split value,

can be chosen in different ways.

For example; For discrete variables it may be useful to choose

the information gain and try to maximize that while for continuous features,

we may want to maximize the reduction in variance.

Decision trees are also easily understood and explained,

since the tree can be used to determine by tracing the decisions made from the root of

the tree to the deciding leaf node how

a final classification is determined for each new instance.

Finally, decision trees are flexible and

can be used for both classification and regression.

The third algorithm introduced in this module,

is the support vector machine or SVM.

Like the decision tree,

this algorithm can also be used for classification and regression.

But the SVM does not generate an easily explainable model.

In classification, SVM is called support vector

classification and works by determining hyper planes that optimally subdivide the data.

For regression, SVM is known as

support vector regression and

the hyper planes are constructed to model the future distribution.

In either case, the construction of these hyper planes is flexible, which allows,

for example some mis-classifications,

as long as the model constructed generalizes appropriately.

This makes SVMs an easy and accurate algorithm to deploy on large complex data.

These three algorithms can either be used directly or

as part of a larger framework in production machine learning systems.

With this module, you will be learning

these fundamental algorithms and thus able to

understand the real world machine learning applications.

I hope you're excited to learn all about them. Good luck.

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