1 COMPSCI 590OP: Applied Numerical Optimization I. COURSE CATALOG This course provides an overview of numerical optimization methods and covers how to implement them in Python. We will discuss common algorithms ranging from gradient descent to stochastic methods, with applications ranging from image processing to neural networks. II. COURSE DESCRIPTION This course provides an overview of the important topic of numerical optimization. In this introductory- level course, we will cover the basic concepts of optimization, the key algorithms, and their applications in image/signal processing, machine learning, and statistical estimation. Topics covered include, but are not limited to: i) the basic ...
36-708: The ABCDE of Statistical Methods for Machine Learning Spring 2021 (Feb 2 to May 6), Syllabus January 29, 2021 1 Basic Course Information Instructor Aaditya Ramdas, aramdas@cmu.edu [Oce hours: 4-5pm T] TA: Ian Waudby-Smith, ianws@cmu.edu [Oce hours: 1-2pm W] Time: 2:20-3:40pm MW Location: Zoom Exceptions: Feb 23 and Apr 15 are university holidays, see the academic calendar. Website See https://36708.github.io/ for basic course material. Announcements All announcements will be made on the above course website. Participants This course can be credited by PhD students with good mathematical background, but it can be audited by anyone ...
36-708: The ABCDE of Statistical Methods for Machine Learning Spring 2022 (Jan 18 to Apr 28), Syllabus January 12, 2022 1 Basic Course Information Instructor Aaditya Ramdas, aramdas@cmu.edu [Oce hours: 4-5pm T] TA: Ian Waudby-Smith, ianws@cmu.edu [Oce hours: 1-2pm W] Time: 1:25-2:45pm TR Location: Zoom OR DH 1211 Exceptions: Apr 7 is a university holiday, Spring break is Mar 7-11, see the academic calendar. Website See https://36708.github.io/ for basic course material. Announcements All announcements will be made on the above course website and/or Canvas. Participants This course can be credited by PhD students with appropriately strong ...
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