Among the topics considered are: data cleaning, visualization, and pre-processing at scale; principles of parallel and distributed computing for machine learning; techniques for scalable deep learning; analysis of programs in terms of memory, computation, and communication complexity; and methods for low-latency inference.

Carnegie Mellon University, Pittsburgh 2025 Spring: Convex Optimization (10725) 2024 Fall: Convex Optimization (10725) 2024 Spring: Convex Optimization (10725) 2023 Fall: Convex Optimization (10725) 2023 Spring: ML with Large Datasets (10405-10605) (with Geoff Gordon) 2022 Fall: Introduction to Machine Learning (10-315) 2022 Spring: Scalability ...

Education Associate: Daniel Bird, (dpbird [at] andrew [dot] cmu [dot] edu), Machine Learning Department. When/where: GHC 4401, 03:30-04:50PM, Mondays and Wednesdays. Recitations: GHC 4401,...

MLG 10405 at Carnegie Mellon University (CMU) in Pittsburgh, Pennsylvania. Large datasets pose difficulties across the machine learning pipeline. They are difficult to visualize and introduce computational, storage, and communication bottlenecks …

We strive to recreate that communal atmosphere among students and instructors. 10405 at Carnegie Mellon University for Spring 2018 on Piazza, an intuitive Q&A platform for students and instructors.

10-417 is undergraduate level and 10-617 is graduate level. Graduate level students will have slightly more work to do than undergraduate level students. If students in 10-417 would like to switch to 10-617, they may do so, but they must reach out to their advisor and cc Dorothy ([email protected]) to get approval.

Machine Learning with Large Datasets. CMU-10-605 has 10 repositories available. Follow their code on GitHub.

2020年10月31日 · Definitely, a great intermediate level ML course if you want some challenge, as most of the rudimentary stuff will only be briefly reviewed most of the time. For pre-reqs, check …

Implement a general framework for developing gradient-descent optimizers for machine learning applications. Explain the differences between, and recognize potential applications of, randomized data structures such as locality sensitive hashing, Bloom filters, random projections, and …

Among the topics considered are: data cleaning, visualization, and pre-processing at scale; principles of parallel and distributed computing for machine learning; techniques for scalable deep learning; analysis of programs in terms of memory, computation, and (for parallel methods) communication complexity; and methods for low-latency inference.