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CP 322
With the rise of Data Science and Big Data, machine learning has been recognized as the key driver behind successful advances of these fields. However, many recent entrants to the field can only utilize the variety of machine learning algorithms as black boxes. This course aims to empower students to effectively use and understand the primary topics in predictive models, including regressions, classifications, neural networks and deep learning, ensemble-based methods, performance evaluation, etc. In addition, the course provides case studies that describe specific data analytics projects, and provide opportunities to employ a variety of machine learning toolboxes. Pre-requisites: CP312.
A tough course to be sure, but the assignments in particular are very helpful to learn the application of machine learning and a few algorithms to get you started.
CP322 was an interesting course, and the content is good. You gotta know your stuff and learn. I usually just put the lectures into NotebookLM and broke them down into easier concepts, super manageable. The assignments help you learn a ton, and the course makes you do a project which you can put on your GitHub, which is fire!