This is a list of all courses offered by the Math Department.  Not all courses are offered each year.  What is provided is a general description of the courses and the prerequisites.  The actual content may vary.

<- Return to Course List

Description

An introductory course connecting students to the most recent developments in the fields of data science and machine learning. The goal of this course is to present in detail the fundamental mathematical ideas behind the data science concepts. In our vision this journey leads naturally to the foundations of machine learning. Students will learn about Exploratory Data Analysis, training, testing and validation, supervised and unsupervised learning, classification and clustering, regression analysis. 


Prerequisites

Minimum grade of C- in MATH241 or MATH340; and minimum grade of C- in MATH240, MATH461 or MATH341; and minimum grade of C- in STAT400 or STAT410


Level of Rigor

Standard


Sample Textbooks

Instructor notes


Applications

Data Science, Machine Learning, Economics, Bioinformatics


If you like this course, you might also consider the following courses

MATH416, MATH420, MATH464, STAT430, STAT440


Additional Notes

Students interested in grad school in STAT should consider this course



Topics

Preprocessing and Data Munging/Wrangling. Normalization. Outliers. Summary Statistics. Regression analysis. Linear models. Least squares. Training, test, and validation datasets. Underfitting. Overfitting. Interpolation and Extrapolation of Data. Support Vector Machines. Linear SVM. Hard and Soft Margin. Kernel trick. Mercer's theorem. Nonlinear SVM. Supervised Learning. Classification. Binary and multiclass classification. Linear classifiers. Nearest neighbors. Unsupervised Learning. Clustering. Centroid-based clustering. K-means. Hierarchical clustering. Decision Trees. Regularization. Data Compression. Dimensionality Reduction. Principal Components. Feature extraction.