Mike Wu is currently a fifth year PhD student at Stanford University advised by Noah Goodman. His research spans the fields of inference algorithms, deep generative models, and unsupervised learning. Mike’s research has appeared in NeurIPS, ICLR, AISTATS, and other top ML conferences with two best paper awards and his work has been featured in the New York Times. Mike previously worked as a software engineer at an AI startup called Lattice Data, and as a research engineer at Meta’s applied machine learning group. Mike and Andrew designed and taught a new version of Stanford’s CS224S: Spoken Language Processing in 2022.
This course is incredibly important and useful! I believe it should be required in any data-science curriculum. We gained practical skills to tackle problems that data scientists and machine learning engineers often face when dealing with real-world messy data. I learned so much more than the course material due to the encouragement and guidance of Mike Wu!
DCDL has taken my experience with ML from modeling datasets in Colab notebooks to working in a full ML system in a codebase. We touched upon the full lifecycle of ML — from annotating and cleaning data, to model training, to evaluation and testing, deployment, and monitoring. What an incredibly insightful 4 weeks of learning!
This final course in the ML track series provided a realistic framework bridging the concepts we have covered in all 3 classes into a more productionalized format. This course has given a real insight into what a real ML backend may look like and the steps required to get there.
