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Dorit Baras is a senior algorithm engineer in Medtronic, where she applies machine learning, deep learning, computer vision and signal processing techniques on medical data. In her previous role she was a research staff member at IBM research lab in Haifa. Her 15 years of experience include projects in various domains such as hardware verification, social network analysis and smart meter data analysis.
She holds a B.Sc. in computer engineering and an M.Sc. in electrical engineering, both from the Technion IIT.
When possible, Dorit likes to give talks on machine learning to teenagers, especially girls, in order to encourage them to choose scientific or engineering career paths. Dorit likes Hello Kitty, attending rock concerts and spending time with her husband and two kids.

Dorit Baras

Sr. Algorithm Engineer
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English, Hebrew
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Haifa, Israel
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Can also give an online talk/webinar
Paid only. Contact speaker for pricing!


Oldies but Goodies – or Why Classical Machine Learning Still Rocks

Data / AI / ML

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The ancient history of deep learning goes back to the 50’s with Rosenblatt’s perceptron, but the real revolution started around 15 years ago (Hinton, Lecun). Deep learning methods now dominate most of the machine learning and AI solutions and outperform classical techniques and even humans on some tasks. However, for those who believe classical methods lost the battle and are no longer relevant, Dorit will prove you wrong!
Classical machine learning has many advantages over deep learning methods in terms of the volumes of required data, interpretability, computational resources and more. In this talk, Dorit will concentrate on the advantages of classical classification methods compared to deep learning classifiers. She will provide concrete examples of problems that can be tackled successfully by classical machine learning. Dorit will also provide guidelines based on her experience of over 15 years in the field on how to choose the best solution whether it is classical, deep or hybrid.

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Oldies but Goodies – or Why Classical Machine Learning Still Rocks







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