MTH 396 Prerequisites: Completion of Tier I Writing Requirement, MTH 309, MTH 310, and MTH 320 (or the honors equivalents, or approval of department) and approval of the department. Typically the department expects a cumulative GPA of at least 2.0 and an average of at least 2.0 across MTH 309, MTH 310 and MTH 320. Note: Email notification will be given once your override has been issued.

Completion of Tier I Writing Requirement and approval of the department. Typically the department expects students to have completed MTH 309, MTH 310, and MTH 320 (or the honors equivalents) with cumulative GPA of at least 2.0 and an average of at least 2.0 across MTH 309, MTH 310 and MTH 320. Additional prerequisite courses may be required and can be found in the descriptions below. Note: Email notification will be given once your override has been issued.

This course provides a broad introduction to machine learning. Topics include supervised learning ( support vector machines, kernels, neural networks) and unsupervised learning (clustering, dimensionality reduction, etc.). We will also discuss the different tasks of machine learning: classification, regression, clustering, density estimation and dimensionality reduction. During the last part of the course, we will discuss some of the recent approaches to machine learning, including those involving the graphical framework.

Prerequisites: MTH 309, MTH 310, & MTH 320

Introduction to Risk Management, Risk Assessment, Risk Financing, and Enterprise Risk Management. An Overview of Insurance Operations, Insurance Regulation, Insurance Marketing and Distribution, the Underwriting Function, Underwriting Property and Liability Insurance, The Claim Function, Adjusting Property and Liability Claims, Actuarial Operations, and Reinsurance.

Prerequisites: MTH 309 & MTH 360

This is an introductory course to Machine Learning (ML). ML is a powerful technique widely used in many big data areas such as insurance, economics, computer vision, bioinformatics, medicine, face recognition etc. In this course, we will not only discuss theoretical framework of ML algorithms and architectures, but also put an emphasis on programing skills so that each student is able to implement ML algorithms for real-world problems. The tentative topics include linear regression, logistic regressions, k-nearest neighbors, k-means, support vector machine, random forests, gradient and boosting trees. If time allows, more advanced methodologies such as manifold regularization and deep neural networks will be discussed.

Prerequisites: STT 442 & CSE 231

Nowadays, it is standard to study groups in a first or second course on abstract algebra. Groups, however, are fundamentally associated to geometric objects. Indeed, every group G permutes its own elements; and, after a choice of generators, this representation can be realized as a the symmetries of a connected graph whose vertex set is G. The purpose of this course is to acquaint the participants with this construction and to embark on a geometric investigation that leads to algebraic results. Numerous connections to other areas of mathematics, including analysis, logic, and topology, will be discussed. Students will be required to given an in-class presentation and to write several drafts and a final draft of a final paper on a related topic. We will use the book "Office Hours with a Geometric Group Theorist" by Clay & Margalit as our primary reference.

Prerequisites: MTH 411 or MTH 418H

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Department of Mathematics

Michigan State University

619 Red Cedar Road

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East Lansing, MI 48824

Phone: (517) 353-0844

Fax: (517) 432-1562

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