Demystifying Machine Learning and Artificial Intelligence

Oct

22

11:00 a.m. - 12:00 p.m. GMT+8

Machine learning. Artificial intelligence. Big data. Industry 4.0.

These buzzwords are everywhere – in newspapers, magazines, TV and podcasts. But what do they mean?

Definitions vary, depending on who you ask. And some of the claims are sensational: Artificial intelligence (AI) and machine learning (ML) are “dark magic” that will soon automate our jobs away. Really?

No. In many cases, these buzzwords are just new names for tried-and-true programs, approaches and techniques, but adapted to take advantage of higher data availability. For example, the supervised and unsupervised learning at the heart of ML rely on well-established statistical modeling techniques: regression, classification and clustering.

Join us for a keynote talk and panel discussion about these topics. You’ll find guidance and stories about how to get started with ML techniques or effectively integrate them within existing programs that seek to make the most of your data and drive more value for your organization.

Featured Keynote: An Introduction to Machine Learning

Bill Myers

Bill Myers

Principal Statistician, Procter & Gamble

Machine learning is an important tool when applied to the right problem. The first step – clearly defining the problem – is vitally important any time you are considering data collection or a statistical analysis. For example, is your goal to develop a statistical model for the purpose of explaining, predicting or possibly both? After outlining a simple definition of machine learning and its terminology, this talk will provide a brief introduction to the two general categories of machine learning, supervised learning and unsupervised learning, and show several examples to provide a better understanding of how machine learning is used in industry. It will also cover critical steps to take before building a machine learning model and advanced methods for more complex or unstructured data.

Industry Talk: Finding a Competitive Advantage With Automation in Data Analytics

Building useful models with data has always been mission-critical for companies striving to compete through innovation. Due to Industry 4.0, big data and digitalization initiatives, you now have more complex data than ever before. Hear from:

Miao Chen

Data Scientist, TEL Singapore

Data analytics plays a key role in helping organizations develop and maintain their competitive advantage – and automation in data analytics becomes increasingly important in many industries. It is a promising step for a variety of functional teams in an organization to overcome the complexity of data sources, ease the shortage of right resources and accelerate the process of extracting value from data. This presentation will provide some basic background on automation in data analytics and share some benefits and techniques of automating analyses using JMP scripts.

What will you learn?

Data is messy. And if the goal of modeling is to increase understanding rather than to simply predict, you also need to know what the data represents. Before you can build a useful model, you need to know how to clean dirty data, make it analysis-ready, and communicate and share your findings to drive action. So a mastery of ML techniques is just one component of solving problems more quickly and driving innovation more effectively.

You'll gain a better understanding of:

 
  • Machine learning terminology, as well as different machine learning categories.
  • How to use machine learning to solve real-world industrial problems.
  • Critical steps to consider before building a machine learning model (for example, data exploration, data quality, dimension reduction).
  • Commonly used methods for classification and regression – from linear regression to ensemble models.
  • Advanced methods, like deep learning, which might be necessary for more complex or unstructured data (for example, images or video).
  • How to automate data analytics to save time and get the most out of your data.
  • The benefits of and techniques for automating analyses.

Meet the Speakers

Back to Top