MATH 255 : Introduction to Machine Learning

Introduces the fundamental concepts and approaches of machine learning, with an emphasis on understanding how to evaluate and formulate problems for data-driven solutions. Students will explore the differences between supervised, unsupervised, and semi-supervised learning, as well as key tasks such as classification and regression. The course covers essential steps in the machine learning workflow, including data preparation, model selection, training, and evaluation. Students will be introduced to commonly used models for tabular data and gain a foundational understanding of deep learning, particularly for unstructured data such as images and text. Emphasis is placed on conceptual understanding and practical application.