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Courses - Summer 2024
Machine Learning
Open Seats as of
06/15/2024 at 09:30 AM
Algorithms and Data Structures for Machine Learning
Credits: 3
Grad Meth: Reg
Provides both a broad coverage of basic algorithms and data structures. Topics include sorting, searching, graph and string algorithms; greedy algorithm, branch-and-bound, dynamic programming and job scheduling; Arrays, linked lists, queues, stacks, and hash tables; Algorithm complexity, best/average/worst case analysis. Applications selected from machine learning problems.
Credits: 3
Grad Meth: Reg
Prerequisite: DATA603 or MSML603.
Cross-listed with: DATA612.
Credit only granted for: DATA612 or MSML612.
Provides an introduction to the construction and use of deep neural networks: models that are composed of several layers of nonlinear processing. The class will focus on the main features in deep neural nets structures. Specific topics include backpropagation and its importance to reduce the computational cost of the training of the neural nets, various coding tools available and how they use parallelization, and convolutional neural networks. Additional topics may include autoencoders, variational autoencoders, convolutional neural networks, recurrent and recursive neural networks, generative adversarial networks, and attention-based models. The concepts introduced will be illustrated by examples of applications chosen among various classification/clustering questions, computer vision, natural language processing.