Update: This article is part of a series where I explore recommendation systems in academia and industry. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, and Part 7.

In information retrieval, evaluation metrics are used to judge and compare the performance of recommendation models on benchmark datasets. Good quantitative assessments of their accuracy are crucial to building successful recommendation systems.

• For a typical offline recommendation problem, we randomly select the training and test samples from the dataset. …

The 51st episode of Datacast is my conversation with Professor Jason Corso — the new director of the Stevens Institute for Artificial Intelligence and the co-founder/CEO of Voxel51. Give it a listen to hear about his wide-ranging computer vision research in image registration, medical imaging, visual segmentation, video understanding, and robotics; his courses at SUNY Buffalo and the University of Michigan; his startup Voxel51 that builds dataset analysis tools; his opinion of doing good research; common threads between professorship and entrepreneurship; and much more.

Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Radio Public, (7) Breaker, and (8)…

Every December since 2015, I have taken some time to write my annual review, a practice that was originally inspired by James Clear (see my 2019, 2018, 2017, 2016, 2015, and 2014 versions). The review's goal is to reflect on the previous year and identify what went well, what could have gone better, and what I’m working toward. This practice enables me to celebrate the efforts and milestones I have made over the past 12 months while examining the bottlenecks and limitations I could have improved upon. The review is a deeply personal report, letting me see myself for who I am and think about the type of person I want to become. This year’s format is inspired by David Perell, which consists of five parts. …

The 50th episode of Datacast is my conversation with Barr Moses — CEO and Co-Founder of Monte Carlo, a data reliability company committed to accelerating the world’s adoption of data by reducing Data Downtime. Give it a listen to hear about her childhood growing up in Israel, her education studying Math and Stats at Stanford, her work experience in consulting at Bain and customer success at Gainsight, her founder journey with Monte Carlo thus far, and much more.

Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Radio Public, (7) Breaker, and (8)…

Last month, I had the opportunity to attend the Toronto Machine Learning Summit 2020, organized by the great people at the Toronto Machine Learning Society. I previously attended their MLOps event in the summer, which I also have written an in-depth recap here.

The summit aims to promote and encourage the adoption of successful machine learning initiatives within Canada and abroad. There was a variety of thought-provoking content tailored towards business leaders, practitioners, and researchers. In this long-form post, I would like to dissect content from the talks that I found most useful from attending the conference.

The post consists of 4…

The 49th episode of Datacast is my conversation with Carl gold— the Chief Data Scientist at Zuora. Give it a listen to hear about his electrical engineering background in Stanford, his Ph.D. work in computational neuroscience at CalTech, his move from academic to working as a quant analyst for a Wall-Street finance firm, his transition into data science, the Subscription Economy Index, “Fighting Churn with Data,” and much more.

Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Radio Public, (7) Breaker, and (8) TuneIn

Key Takeaways

Below are highlights from my conversation with…

The 48th episode of Datacast is my conversation with Jessie Smith — a Ph.D. student at The University of Colorado Boulder researching machine learning and AI ethics with an emphasis on algorithmic fairness and transparency. Give it a listen to hear about her foray into Computer Science Ethics, her involvement with the open data movement, her research on bias and fairness for recommendation systems, her public scholarship via Radical AI and SciFi For Real Life, and more.

Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Stitcher, (5) iHeart Radio, (6) Radio Public, (7) Breaker, and (8)…

The 47th episode of Datacast is my conversation with Luis Serrano — a Quantum AI Research Scientist at Zapata Computing. Give it a listen to hear about his academic background in mathematical combinatorics, his work building the YouTube recommender system at Google, his passion for teaching machine learning via Udacity and Apple, his popular YouTube videos, his interest in quantum computing, his upcoming book “Grokking Machine Learning” with Manning, and more.

Listen to the episode on: (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) Radio Public, (5) iHeart Radio, and (6) TuneIn

Key Takeaways

On Studying Math at Waterloo

• In the beginning, I was actually terrible at math. It was my least favorite subject in schools. I somehow loved it, and I didn’t know, as I was always playing with puzzles and logic games, which I didn’t think were related to math. …