It is hard to imagine anything more fascinating than automated systems that improve their performance. The study of learning from data is commercially and scientifically crucial for members of a modern society that has machine-learning applications in almost every system we encounter. This course provides a thorough grounding in machine learning methodologies, technologies, and algorithms. The course topics include classical statistics, machine learning, data mining, and statistical algorithms. It will also equip you with tools and examples to decide when using machine learning for a particular task is appropriate or effective.
The course is broken up into five overarching case studies
- Regression
- Classification
- Clustering and Similarity
- Recommender Systems
- Deep Learning
Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms. However, the class has been designed to allow students with a strong numerate background to catch up and fully participate.