Anomaly Detection
Taught by Dr. Inbal Yahav

Anomaly Detection

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Aim of Course:
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.
Anticipated learning outcomes:
  • Determine how to apply a supervised learning algorithm to a classification problem for anomaly detection
  • Use software to implement such a model
  • 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
  • Use characteristics of the data and its originating domain to make judgments about which methods among a diverse set work best to identify anomalies
  • Practice applying the various techniques to different problems in different domains
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

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
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.

Anomaly Detection

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.
You should be familiar with supervised and unsupervised learning techniques, as covered in Predictive Analytics 1, 2 and 3. 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 (and with sklearn) is needed, particularly since support for Python in the class is limited.
Organization of the 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 Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses


College credit:
Anomaly Detection 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 Note: The decision to accept specific credit recommendations is up to each institution. More info here.

This course is also 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 .
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.

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


October 16, 2020 to November 13, 2020 October 15, 2021 to November 12, 2021

Anomaly Detection


October 16, 2020 to November 13, 2020 October 15, 2021 to November 12, 2021

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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