The Data Scientist Show - Daliana Liu
Bayesian thinking in work and life, ad attribution models and A/B testing, machine learning@Foursquare - Max Sklar - the data scientist show050
Sep 13, 2022
01:30:25
AI Summary
Highlights
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Choosing the right problem and collecting relevant data are crucial for effective decision-making in data science.
- The exploration of abstract mathematics and its practical applications in data science can lead to innovative solutions.
Deep dives
The Journey into Machine Learning
The podcast episode covers Max's career journey from a junior engineer to a senior engineer, with a focus on his transition into machine learning. Max studied computer science, which provided a broad range of skills applicable to various domains. He became intrigued by the idea of machine learning and how machines could automate code writing. He further explored machine learning during his graduate studies, focusing on probability theory and Bayesian inference. Max applied Bayesian inference in real-world projects, such as building a rating system and measuring ad effectiveness. He emphasizes the importance of considering priors and constructing principled models, and his work on sampling bias correction for supervised machine learning.
