Episode 47: Math and Machine Learning In Pedestrian Terms with Luis Serrano
Datacast
English - November 09, 2020 16:00 - 51 minutes - 45.2 MB - ★★★★★ - 4 ratingsTechnology Business Careers research data engineering data science artificial intelligence machine learning statistics technology startup computer science venture capital Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Show Notes
(2:12) Luis shared how he got excited about learning mathematics and specialized in combinatorics.(4:26) Luis discussed his experience studying Math for his Bachelor’s and Master’s degrees at the University of Waterloo - where he took many courses in combinatorics and engaged in undergraduate research.(5:59) Luis pursued his Ph.D. in Mathematics at the University of Michigan - where he worked on Schubert Calculus that intersects combinatorics and geometry (check out his Ph.D. dissertation).(8:45) Luis distinguished the differences between doing research in mathematics and machine learning.(11:33) Luis went over his time as a Postdoc Fellow and Lecturer at the University of Quebec at Montreal - where he was a member of the LaCIM lab (whose areas of research originating in Combinatorics and its relationships to Algebra and Computer Science) and taught classes in French.(13:47) Luis explained why he left academia and got his job as a Machine Learning Engineer at Google in 2014.(16:33) Luis discussed the engineering and analytical challenges he encountered as part of the video recommendations team at YouTube.(19:58) Luis shared lessons he learned to transition from academia to industry.(22:25) Luis went over his move to become the Head of Content for AI and Data Science at Udacity, alongside his online education passion.(26:08) Luis explained Udacity's educational approach to course content design in various nano degree programs, including Machine Learning, Deep Learning, and Data Science.(28:46) Luis unpacked his end-to-end process of making YouTube, where he teaches concepts in Machine Learning and Math in layman terms.(31:01) Luis unpacked his statement, "Humans are bad at abstraction, but great at math," from his video “You Are Much Better At Math Than You Think.”(34:46) Luis shared his 3 favorite Machine Learning videos: Restricted Boltzmann Machines, A Friendly Introduction to Machine Learning, and My Story with the Thue-Morse Sequence.(37:18) Luis discussed the data science culture at Apple, where he spent one-year teaching machine learning to the employees and doing internal consulting in AI-related projects.(39:06) Luis revealed his interest in quantum computing. He works as a Quantum AI Research Scientist at Zapata Computing, a quantum software company that offers computing solutions for industrial and commercial use.(43:19) Luis mentioned the challenges of writing “Grokking Machine Learning” - a technical book with Manning planned to be published next year - like a mystery novel.(46:12) Luis shared the differences between working in Silicon Valley and Canada.(47:50) Closing segment.His Contact Info
WebsiteTwitterLinkedInYouTubeGitHubGoogle ScholarMediumHis Recommended Resources
Sebastian ThrunAndrew NgRana el Kaliouby"How Not To Be Wrong: The Power of Mathematical Thinking" by Jordan Ellenberg"Weapons of Math Destruction" by Cathy O'NeilHere are the codes for free eBook copies of Luis' book "Grokking Machine Learning": gmldcr-D659, gmldcr-2512, gmldcr-0752, gmldcr-30A2, gmldcr-01E8. Additionally, use the code poddcast19 to receive a 40% discount of all Manning products!
About the show
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
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