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naperlou
Member Since: August 3, 2011
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September 16 - Day 5: Optimization
Continuing Education Center 
9/16/2016  342 comments
One major feature of the IoT is that we now have lots of data over long periods of time for many types of systems.We can use this rich data horde to perform optimizations in current systems and in the design of new or evolved systems.These techniques often use complex formulations involving linear and non-linear systems approaches.Fortunately, there are many existing implementations of these methods that make their execution relatively easy.We will look at them in a couple different languages.
September 15 - Day 4: Failure Analysis & Anomaly Detection
Continuing Education Center 
9/15/2016  446 comments
For this lecture we will talk about failure detection and anomaly detection, and the analyses used in conjunction with them. In the IoT we often want to perform these on a continuous basis. This introduces some interesting features in the analysis that we will discuss.
September 14 - Day 3: Classification
Continuing Education Center 
9/14/2016  422 comments
A major area of machine learning concerns classification. We will describe both supervised and unsupervised learning techniques and discuss when and how to use them. Techniques include k-means clustering, support vector machines, principal components analysis and decision trees, for example. We will also look at applications of these techniques.
September 13 - Day 2: Languages and Environments
Continuing Education Center 
9/13/2016  510 comments
To implement the analytics we are discussing this week, one needs software and a platform to run it on. We will discuss languages such as R and Python and more conventional languages and traditional statistical processing systems. Because the size of the data is often massive and growing, platforms become important. It is key to pick one that can start small and grow in power without having to completely rewrite the application. We will give an overview of platforms specific to the IoT.
September 12 - Day 1: Introduction to Analytics for the IoT
Continuing Education Center 
9/12/2016  480 comments
Today we will present an overview of the types of statistical techniques useful for the IoT and how statistics is used in this context. Some differences with standard statistics will be highlighted. The requirements for IoT analytics will also be presented. Our intent is to discuss the algorithms in the context of engineering applications in the IoT.
March 11 - Day 5: Machine Learning & Analysis Techniques
Continuing Education Center 
3/11/2016  511 comments
One of the most important statistical techniques used in analyzing IoT data is machine learning. In this final class, we will discuss some important techniques, such as support vector machines and unsupervised learning. An understanding of the techniques is important in determining what can be done in applications that design engineers may encounter.
March 10 - Day 4: Examples of Big Data Analysis
Continuing Education Center 
3/10/2016  485 comments
Now that the things have been deployed, and the data communicated and stored, we need to analyze. We will look at some examples of how this analysis is performed and used. These examples will include analysis with feedback (i.e., higher level control), analysis used for product improvement (e.g., PLM) and analysis used to understand consumer use. These last two are related, but often are done by separate groups with different goals.
March 9 - Day 3: Cloud Storage and Formats in the IoT
Continuing Education Center 
3/9/2016  572 comments
Generating and communicating data is great, but we need to have somewhere to keep it. In the current environment, the perfect place is the cloud. This is where most IoT data will go and where it will be analyzed. The cloud provides ease of access, flexibility, redundancy, and unlimited growth potential. We will discuss the storage structures and Big Data formats that are used.


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