Applied Machine Learning and Time-Series Analysis

UST Room #302

1:00 pm - 2:00 pm

Technical Workshop

How This Fits Into IoT
Algorithms are a core part of any IoT application. They convert the collected data into actionable decisions. Machine learning plays a key role in algorithm development for IoT applications. In addition, what makes IoT data different is that a significant portion of the data is obtained over time. Thus, the data is inherently of time-series nature.

Although the classical machine learning algorithms can be applied to this type of data, there are certain classes of algorithms that are well-suited to tackle data sets with time dependency, such as recurrent neural networks.

These algorithms can be leveraged via the open source packages written in Python and other languages.

Here is the outline of the workshop:
a. Brief introduction to machine learning
b. Hand-on development of machine learning models for a sample data set using Python
c. Introduction to time-series analysis and existing tools
d. Hands-on development of a recurrent neural network
e. Best practices, tips, and challenges when dealing with time-series data

What Attendees Do
Attendees will apply various machine learning models (including classical and time-series specific) using Python to a sample data set.

Learning Objectives
1. Understand basic steps in developing machine learning models
2. Learn about the different types of time-series problems
3. Learn how to develop machine learning models using Python
4. Practical tips and tricks when dealing with time-series data

What Attendees Bring

Attendee Preparation Work (Downloads, Reading)
Install Python through Anaconda. Install tensorflow and Keras.

Knowledge Required
Nothing specific, but some familiarity with Jupyter Notebook environment helps (e.g.

Pre-class Set-up
The projector with HDMI connectors, internet access instructions, white board and markers

What Attendees Receive
Hands-on training, no giveawaysLink to Image of Giveaway if Applicable

Links and Additional Reading Material

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