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 pro blem 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:
 
Homework in this course consists of short answer questions to test concepts and guided data analysis problems using Python.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.
Level:
Intermediate
Prerequisite:
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:

Options for Credit and Recognition:

 

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.
Instructor(s):

Dates:

October 18, 2019 to November 15, 2019 October 16, 2020 to November 13, 2020 October 15, 2021 to November 12, 2021

Anomaly Detection

Instructor(s):

Dates:
October 18, 2019 to November 15, 2019 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: Click here 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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