AI News, TDWI | Training Research | Business Intelligence, Analytics, Big Data, Data Warehousing

TDWI | Training Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Academic programs are one way for professionals to stay current with today's most in-demand skills.

Today its Short Programs draw more than 1,500 students each summer to short courses across topics ranging from innovation and entrepreneurship to biotechnology, information technologies, data modeling, and systems engineering.

Its new slate of AI and machine learning courses for the coming summer will be appreciated by professionals in the global community who need to come up to speed as AI techniques are applied to an increasing array of disciplines.

The overall aim of the program is to provide the tools for leaders and managers of professional departments to broaden their understanding of technologies to ensure that new technology can be adequately integrated into business processes.

The goal of MIT Professional Education is to separate the wheat from the chaff, helping people understand the big themes, how they relate to their own products and services, what they need to learn, and to acquire the broad set of skills needed to lead teams better and make better decisions.'

Take, for example, the Modeling and Optimization for Machine Learning course offered by MIT Professors Justin Solomon and Suvrit Sra that reduces engineering and computational problems to their standard mathematical forms to determine which algorithms and software tools will best solve them.

Because an important part of optimizing machine learning is developing the basic approach, modeling, optimization, and algorithm selection are critical to integrating deep learning and other machine learning into the business environment.

According to Justin Solomon, 'We will take a group of professionals in areas related to data science, get them up to speed with modeling and optimization, and walk through common scenarios and modern tools.

We will lead the class through particular applications, examining optimization problems, experimenting with state-of-the-art tools, and developing an understanding that can be extended to computer vision, computational biology, language, modeling, and other areas familiar to machine learning.'

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