EECE5644-Machine_Learning and machine_learning_resources
These are complementary course materials from the same Northeastern University EECE 5644 class, where one repository provides lecture code implementations while the other provides supplementary learning resources and Jupyter notebooks to reinforce the same curriculum.
About EECE5644-Machine_Learning
rohinarora/EECE5644-Machine_Learning
Graduate course on Machine Learning
This is a collection of lecture notes and materials for a graduate-level machine learning course. It covers foundational topics like linear algebra, probability, and optimization, then progresses into core machine learning concepts such as PCA, Bayesian decision theory, and Expectation-Maximization. The intended user is a graduate student or researcher looking to learn or review advanced machine learning principles.
About machine_learning_resources
mazrk7/machine_learning_resources
Machine learning resources (Jupyter notebooks mostly). Originally code to complement the "EECE 5644: Introduction to Machine Learning and Pattern Recognition" course taught at Northeastern University.
This collection of Jupyter notebooks provides practical examples and code implementations for fundamental machine learning and pattern recognition concepts. It helps students understand complex algorithms by showing both the mathematical theory and how to code them in Python. Students taking an introductory machine learning course would find this useful for hands-on learning.
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