| Home | Calendar | Contacts | Handouts | Feedback | MS&E Dept |


Class meets Mondays, Wednesdays, Fridays, 9:30-10:45, in Herrin T175.
The syllabus, study guide, lecture notes, and solution sets are posted on the Handouts page.

There will be problem sessions starting Friday, Sept 26 at 1:15 and 2:15 in 200-030, and Sunday, Sept 28 7-9pm in Thornton 110.

Students can register for the class on Axess but not for sections. Do not be alarmed by the warning that there is no capacity in the sections.

Course Objective

This is a fast-paced, fundamental course designed to develop an understanding of uncertain phenomena using the theory of probability. The course objective is to provide students with conceptual and intuitive insights into probabilistic reasoning and the ability to understand and solve real world problems.

Intended Audience

For students seeking an introduction to probability theory and applications, this course is designed to develop their intuition and model building skills. You should acquire Ways of Thinking in Formal Reasoning (intuitively understand a number of fundamental probabilistic reasoning concepts based on a mathematical foundation) and Applied Quantitative Reasoning (solve real world problems under uncertainty by structuring them, building models, and analyzing those models). This course also satisfies the Distributional Breadth GER in Engineering and Applied Science.

It is intended for undergraduate students and should be taken for five units.
Graduate students in MS&E should enroll in a similar but separate course, MS&E 220.

Course Summary

Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: the structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, engineering, and physical problems provide motivation and illustrations of modeling techniques. Spreadsheets will be used to illustrate and solve problems as a complement to analytical closed-form solutions.

Topics include axioms of probability, probability trees, belief networks, random variables, distributions, conditioning, inference, expectation, change of variables, and limit theorems.

Prerequisite: Mathematics 51.

Required Textbook

The required textbook for the course is Sheldon Ross, A First Course in Probability, Prentice Hall, 2014 (Ninth Edition). It is on reserve in the Engineering Library, and it is possible to use the eighth edition instead.


Additional information is in the syllabus posted on the Handouts page and at syllabus.stanford.edu.