Anomaly Detection

Anomaly Detection

In this course you will learn how to examine data with the goal of detecting anomalies or abnormal instances.

In this course you will learn how to examine data with the goal of detecting anomalies or abnormal instances.

549
Alert me to upcoming courses

Overview

In this online course, you will learn how to examine data with the goal of detecting anomalies or abnormal instances. This task is critical in a wide range of applications ranging from fraud detection to surveillance. At the end of this course, you will have understood the different aspects that affect how this problem can be formulated, the techniques applicable for each formulation and knowledge of some real-world applications in which they are most effective.

Learning Outcomes

This course is ideal for data scientists, business analysts, medical personnel, security specialists, statisticians, software engineers, technical managers interested in learning statistical methods to identify anomalies, appropriate techniques for handling them and the range of applications in which they occur.

  • Determine how to apply a supervised learning algorithm to a classification problem for anomaly detection
  • Explain the limitations of supervised learning for anomaly detection
  • Explain the advantages and disadvantages of various statistical methods for identifying anomalies in the absence of labels
  • Apply and assess a nearest-neighbor algorithm for identifying anomalies in the absence of labels
  • Practice applying the various techniques to different problems in different domains
  • Apply a supervised learning algorithm to a classification pro blem for anomaly detection
  • Explain the limitations of supervised learning for anomaly detection
  • Make judgments about which methods among a diverse set work best to identify anomalies

Who Should Take This Course

Data scientists, business analysts, medical personnel, security specialists, statisticians, software engineers, technical managers interested in learning statistical methods to identify anomalies, appropriate techniques for handling them and the range of applications in which they occur.

Instructors

dr-inbal-yahav

Dr. Inbal Yahav

Dr. Inbal Yahav is a faculty member at the Graduate School of Business Administration, Bar-Ilan University, Israel.  Her research interests lie in the areas of statistical modeling and social media, with a focus on users' behavior in social networks, interactions and dynamics among users, and statistical modeling of heterogeneous behaviors.  Dr. Yahav's research to-date focuses on two domains. The first domain is cyber security and privacy, and in specific privacy unawareness and unintentional information leakage in social networks. The second domain is statistical modeling of sub-populations in big data. Dr. Yahav has presented her work at multiple Information Systems conf...
See Instructor Bio

Course Syllabus

Week 1

Getting Started

  • The different aspects of anomalies
  • Classification-based approaches

Week 2

Unsupervised Approaches

  • Clustering
  • Nearest-neighbour
  • Other statistical techniques

Week 3

Non-Standard Approaches

  • Information-theoretic methods
  • Spectral techniques

Week 4

Applications

  • Credit-card fraud
  • Intrusion detection
  • Insurance
  • Healthcare
  • Surveillance

Class Dates

2020

Oct 16, 2020 to Nov 13, 2020

2021

Oct 15, 2021 to Nov 12, 2021

2022

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

You should be comfortable with reading technical papers from peer-reviewed journals and conferences in Artificial Intelligence. You should be comfortable using R and associated data science packages. If you choose the Python option, familiarity with the software is needed since support for Python in the class is limited.

You should be familiar with supervised and unsupervised learning techniques, as covered in these courses, however prior enrollment in these courses are not required for enrollment in Anomaly Detection:

What Our Students Say​

As always it was a pleasure taking a course from Professor Indurkhya. This course was a model of what Statistics.com courses can be.

Ullash Hazarika
Oxford Decisions

The subject of identifying anomalies is a central task of academic historians, and this course allowed me the welcome luxury to reflect on their nature and methods of discovery.

Milan Hejtmanek
Seoul National University

Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Deep Learning

This course will introduce you to the essential techniques of text mining as the extension of data mining’s standard predictive methods to unstructured text.
Topic: Data Science, Machine Learning | Skill: Intermediate | Credit Options: ACE, CEU
Class Start Dates: Nov 6, 2020

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of short answer questions to test concepts and guided data analysis problems.  There is also an end-of-the-course data analysis project.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Text

Outlier Analysis by Charu Aggrawal. It will be supplemented by other technical papers available online. If you want a reference for programming, Python for Data Analysis is recommended. This course has supplemental readings that are available online.

Software

Course illustrations and support are in R.  There are also illustrations and model answers in Python, but there is only limited support for Python.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

Register for This Course​

Anomaly Detection
549
Get Notified

Have a Question About This Course?

Janet Dobbins

Sales and Business Development

Phone

(571) 281-8817

Send Us A Note

We like to hear from you.

Name*

Email*

Phone

Company

Message*

 

Send
Close Menu