Program Overview

Industrial Engineering & Management Sciences

The Industrial Engineering & Management Sciences department offers various programs, including undergraduate and graduate degrees.

Academics

The department provides a range of academic programs, including:

• Undergraduate Program
  • Industrial Engineering Major (BS Degree)
  • Curriculum
    • Degree Requirements
  • Advising and Mentoring
  • Changing Majors to Industrial Engineering
  • Student Organizations & Professional Societies
  • Extracurricular Activities
  • BSIE Goals & Outcomes
  • Client Project Challenge
    • Submissions
    • Important Dates
• PhD Program
  • Prospective Graduate Students
    • Recent Alumni Placement
  • Curriculum & Focus Areas
  • PhD Admissions
    • Admission Criteria & Background
    • Application Procedures
    • Preparation for Graduate Study
    • Funding Information
    • Frequently Asked Questions
    • Applicant Information Weekend
• Professional MS Programs
• Courses
  • Previous Years' Schedules

Research

The department is involved in various research areas, including:

• Applied Statistics & Statistical Learning
• Financial Engineering
• Healthcare Engineering
• Optimization
• Computational Social Science
• Logistics & Operations
• Stochastic Analysis & Simulation
• Research Centers
• Grants and Projects

People

The department consists of:

• Faculty
  • Core Faculty
  • Emeritus Faculty
  • Affiliated Faculty
  • Faculty Awards & Honors
• Advisory Board
• Graduate Students
  • PhD Graduates (2000 on)
  • PhD Students on the Job Market
• Staff
• Researchers
• Visitors
• Distinguished Alumni

News & Events

The department hosts various events, including:

• News
  • All News
  • News Archive
• All Events
• Department Seminars
• Center for Optimization and Statistical Learning Seminars
• Wasserstrom Lecture Series
• Newsletter

Course Descriptions

IEMS 302: Probability

Prerequisites

Co-requisite: Math 228-2

Description

Introduction to probability theory and its applications. Conditional probabilities and expectation values. Random variables and distributions, including binomial, Poisson, exponential, and normal. Joint distributions and limit laws for foundation of and connection to statistics. Examples in reliability, inventory, finance, and statistics.

Learning Objectives

• Students will know and be able to apply the axioms of probability
• Students will understand the properties of probability distributions and will be able to use them to compute relevant probabilities
• Students will be able to model some problem contexts using an appropriate probability distribution
• Students will be able to recognize and utilize independence, and will understand the limitations when independence does not hold
• Students will be able to identify and apply conditional probabilities

Topics

• Basic probability concepts, events and random variables
• Conditional probability and independence
• Discrete and Continuous Random Variables, probability functions
• Independent trials; Binomial, Geometric, and Poisson distributions
• Uniform, Exponential, and Normal distributions
• Joint distributions, conditional distributions
• Limit Theorems

Materials

Introduction to Probability by Blitzstein and Hwant.