Though the term “machine learning” has become increasingly common, many still don’t know exactly what it means and how it is applied. We will examine how machine learning is defined as a tool used by data scientists — and take a bird’s eye view of how it was developed, how it is currently being used, and what lies ahead as it continues to evolve.
The torrent of data generated from machines, networks, devices, and data centers in industry verticals provide challenges and opportunities. The challenge is to make this machine data meaningful and actionable to deliver on opportunities around operational efficiencies. On March 15, Beena Ammanath, Vice President of Data Science at General Electric, joined datascience@berkeley to present “Industrial Internet: Big Data and Analytics Driving Big Outcomes.” Ammanath shared real-world case studies demonstrating tangible operational benefits by tightly integrating machines, networked sensors, industrial-strength data, and software to enable intelligent insights and affect measurable outcomes. Here, we provide a recap of her presentation and key takeaways about how the Industrial Internet is making the most of big data to drive big outcomes.
The Master of Information and Data Science (MIDS) program at the UC Berkeley School of Information (I School) culminates with a synthetic capstone project. One capstone team consisting of MIDS students Nicholas Hamlin, Natarajan Krishnaswami, Glenn Dunmire, and Minhchau Dang came together to create AidSight. AidSight is a platform that uses modern data science techniques to make the 600,000 aid activities reported to the International Aid Transparency Initiative (IATI) understandable and digestible at a glance. The AidSight team took some time to answer a few questions we had about the impact their platform is having on the global aid and funding community.
On December 19th, the fall 2016 datascience@berkeley graduates presented their capstone projects in a public webinar. For the capstone project, the graduates were tasked to solve a real-world situation or problem utilizing their data science skills in communication, problem-solving, influence, and management to provide a fully realized solution.
Machine learning continues to deepen its impact with new platforms that enable more efficient and accurate analysis of big data. One such platform is Apache SystemML, which allows for large-scale learning on the underlying Apache Spark platform, while maintaining the simple, modular, high-level mathematics at the core of the field. In a recent webinar, Mike Dusenberry, an engineer at the IBM Spark Technology Center presented the work he and his team are doing to create a deep learning library for SystemML and solve for performant deep learning at scale. Here, we’ll provide the key points that Mike discussed, as well as additional resources for further exploration.
On August 25, the summer 2016 datascience@berkeley graduates presented their capstone projects in a public webinar. For the capstone project, the graduates were tasked to solve a real-world situation or problem utilizing their data science skills in communication, problem-solving, influence, and management to provide a fully realized solution.