INTRO TO MACHINE LEARNING
Course DescriptionThis introductory machine learning course will give an overview of many models and algorithms used in modern machine learning, including linear models, multi-layer neural networks, support vector machines, density estimation methods, bayesian belief networks, clustering, ensemble methods, and reinforcement learning. The course will give the student the basic ideas and intuition behind these methods, as well as, a more formal understanding of how and why they work. Through homework assignments students will have an opportunity to experiment with many machine learning techniques and apply them to various real-world datasets.
Credits:3View this course on Pitt's Website
Term Spring 2020Professor Joseph P. Yurko Difficulty 2/5Quality 4/5
Dr.Yurko was an excellent teacher. Initially, you may find yourself a little overwhelmed with all of the math and statistics concepts, but if you dedicate the time to go through his slides, and do a bit of independent research, you will do fine. His homework assignments are in R, but most of the code is provided for you. His assignments are more about interpreting the data than the code itself.
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