Fundamentals of Machine Learning: Perspectives from a Data Scientist

Fundamentals of Machine Learning: Perspectives from a Data Scientist

April 12, 2018, 1:15 PM - 2:15 PM

Location: Sands 305 Level 1
Language:
English

As our world grows more connected, organizations are collecting ever-growing amounts of data. Almost always there are hidden insights in such data that can lead to better outcomes and more value. One important tool to tap into these opportunities is Machine Learning (ML), and across all verticals more and more companies are investing into their ML operations. In this talk, we will take a look at what ML is, what problems it solves, how it is applied, and why companies need to make sure that they have a strategy to employ ML.
First, we will explain the relevant fundamental concepts with a focus on supervised learning and geometric models. An intuitive data set with an accessible instance space from the physical world is used to illustrate our ability to classify data. Various models are used and visually represented to explain the underlying algorithms in an accessible fashion.
Next, we will discuss how ML is revolutionizing approaches to cybersecurity, and how the cybersecurity industry has been changing its approach to the data it collects. From there, we explore other applications in the larger domain of security.
Lastly, we will wrap up with an outlook of where this technology is going and some pointers to get started with employing ML to the data you already collect.

Learning Objectives:
1. Develop an intuition for the hidden value in data and understand ML as a tool to unlock it.
2. Understand how ML works under the hood and why it is a necessity for any data-driven business.
3. Understand the current applications of ML and get ready to start using ML on existing data.

Contributors

  • Sven Krasser

    Presenter

    Chief Scientist

    CrowdStrike

    Dr. Sven Krasser currently serves as Chief Scientist at CrowdStrike where he leads the machine learning efforts utilizing CrowdStrike’s Big Data...

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