Introduction to Machine Learning

Catalog description

Machine learning is a sub-field of Artificial Intelligence that is concerned with the design and analysis of algorithms that "learn" and improve with experience, While the broad aim behind research in this area is to build systems that can simulate or even improve on certain aspects of human intelligence, algorithms developed in this area have become very useful in analyzing and predicting the behavior of complex systems. Machine learning algorithms have been used to guide diagnostic systems in medicine, recommend interesting products to customers in e-commerce, play games at human championship levels, and solve many other very complex problems. This course is an introduction to algorithms for machine learning and their implementation in the context of big data. We will study different learning settings, the different algorithms that have been developed for these settings, and learn about how to implement these algorithms and evaluate their behavior in practice. We will also discuss dealing with noise, missing values, scalability properties and talk about tools and libraries available for these methods. Finally, we will discuss the potential biases of machine learning algorithms when used for decision making and how to mitigate them to improve the fairness of the decisions. At the end of the course, you should be able to: -Understand when to use machine learning algorithms; -Understand, represent and formulate the learning problem; -Apply the appropriate algorithm(s) or tools, with an understanding of the tradeoffs involved including scalability and robustness; -Correctly evaluate the behavior of the algorithm when solving the problem. -Identify potential sources of bias in a trained machine learning model and how to mitigate them. Prereq: ( CSDS 132 or CSDS 134 ) and ( MATH 122 or MATH 124 ) and ( MATH 201 or MATH 307 ) and ( MATH 380 or STAT 301 or STAT 312 or STAT 313 or STAT 332 ).