CSCI 370
Machine Learning: Algorithms and Applications Spring 2022
Division III Quantitative/Formal Reasoning
This is not the current course catalog

The course introduces students to the fascinating field of Machine Learning. Machine Learning is a subset of Artificial Intelligence which studies learning algorithms – the algorithms that once set up make it possible for a machine to learn on its own without being explicitly instructed. The first half of the course (Algorithms) covers basic Machine Learning algorithms and the Math behind them: Stochastic Optimizations, Decision Trees, Memory-based Reasoning, Bayesian Belief Networks, Clustering, Associations, and Neural Nets. We will follow the logic behind these algorithms and implement them in pure Python. Students are expected to have some background in Statistics and Linear Algebra, however all the required Math fundamentals will be provided in the form of short primers. The second part (Applications) is fully dedicated to solving real-life predictive problems in a series of extensive Data Science labs. We will use the implementation of the ML algorithms from the sklearn Python library. This exploratory activity culminates in an open-ended student project inspired by student interests. Students will be exposed to applications of Machine Learning to Science, Humanities and real-life. By the end of the course they will acquire a toolbox for exploring a modern world full of digital data and making their own discoveries. The ambitious far-reaching implicit objective includes developing interest in Math as a discovery tool, learning how to handle ambiguity, and better understanding and formalizing mental models of learning.