**Intelligence and Security Analytics** - Statistical and machine learning methods for detecting anomalies, identifying images, and processing data from sensors.

**Anomaly Detection, online course taught by Nitin Indurkhya, Oct 21 - Nov 18, 2016**

Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such “training” data are available. In such cases, the goal is to identify cases that are very different from the norm.

Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

**Deep Learning, online course taught by Alan Blair and Nitin Indurkhya, Nov 18 - Dec 15, 2016**

In this online course, you will learn about the rapidly evolving field of Deep Learning. The surge in deployed applications based on concepts and methods in this field is an indication of its potential to help fully realize the promise of Artificial Intelligence. At the end of this course you will understand the basic concepts underlying the representations and methods in deep learning and see some applications where deep learning is most effective. You will also gain an appreciation of what kind of problems are most suited for this field and current research trends.

**Internet of Things (IoT): Programming for Analytics, online course taught by Ajit Jaokar Oct 14 - Nov 25, 2016**

The "Internet of Things" (IoT) is the massive network of sensors and devices that produce data that can be accessed over the internet. It is sometimes considered the next big wave of "Big Data," the first being the exploitation of huge organizational databases and the second being the generation of unstructured data by social applications.

This 6 week course will review the flow of IoT data, where analytics fits in, and the wide range of applications where IoT plays a role. You will use python to perform analytics on a stream of IoT data; the course contains a review of the relevant python machine learning libraries. The course concludes with a couple of case studies.

**New online R course:**

Meta Analysis in R, online course taught by Dr. Stephanie Kovalchik starts Oct 21 - Nov 18, 2016

Meta-analysis, the ‘analysis of analyses’, is the term used to describe the quantitative synthesis of scientific evidence. The aim of this course is to introduce students to the fundamentals of meta-analysis and provide an in-depth review of tools for conducting meta-analyses in the R language. The course will cover the fundamentals of the fixed and random effects models for meta-analysis, the assessment of heterogeneity, and evaluating bias. Advanced topics will include the handling of rare events, missing data, and indirect treatment comparisons, among other topics.

The course assumes introductory knowledge of R. There will be a brief review of R programming in the first part of the course and links to other statistics.com courses for those who need a more extensive refresher.

After completion of this course, students will know how to apply standard methods of meta-analysis in R and will also have gained more experience with advanced R programming topics, such as function writing and reproducible reporting.