What you'll learn

They will have an understanding of the imbalance learning. At the end of this course they will be able to answer questions: What causes this problem? What are the different characteristics of unbalanced dataset? What are the algorithms and techniques to tackle this problem? Which one to use for their dataset? And finally, how do you evaluate a solution in such area?

Requirements

Prior knowledge in machine learning/data science is necessary or at least currently enrolled in a machine learning course.

Description

There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from imbalanced data poses major challenges and is recognized as needing significant attention.

The problem with imbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions.

The specific goals of this course are:

Help the students understand the underline causes of this problem.

Go over the major state-of-the-art methods and techniques that you can use to deal with this problem.

Explain the advantages and drawback of different approaches .

Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.

Who this course is for:

This course is for students and professionals who are working in the machine learning/data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning.