This preview shows pages 1–3. Sign up to view the full content.
Instructor:
Alessandro Rinaldo
Oﬃ
ce:Baker
Hall 229I
Email:
arinaldo@stat.cmu.edu
Oﬃ
ce
Hours:Wednesday, noon  1:00pm, Baker Hall 229I
Daniel Park. Oﬃ
ce
Hours: Tuesday, 2:00pm3:00pm, FMS 320.
Zhanwu Liu. Oﬃ
ce
Hours: Wednesday, 2:00pm5:00pm, FMS 320.
Introduction to Probability, by D.P. Bertsekas and J.N. Tsitsiklis,
Athena Scientifc, 2008 (2
nd
edition)
Tuesday and Thursday, 10:30am  11:50am, PH100.
due by 4:00pm on Thursday in BH132, unless otherwise instructed.
September, 28 and November, 2.
TBA.
Class is cancelled on August, 31.
21118 or 21122 or 21123 or 21256 or equivalent course.
Basic rules o± integration in 2 dimensions.
Not open to students who have received credit ±or 36225 or 36625.
Teaching Assistants:
Textbook:
Class Meetings:
Weekly assignments:
Midterm Exams:
Final Exam:
Special date:
Prerequisites:
Course Description
The theory o± probability and random processes provides the mathematical tools and ±ormalism
needed to model uncertainty in virtually all scientifc areas. Nowadays, probabilistic models and
statistical theory are essential ±or the development and analysis o± innumerable applications, rang
ing ±rom the analysis o± network dynamics o± circuit ±ailure rates, the development o± algorithms
±or computer vision or machine learning, image processing, cryptography, system per±ormance as
sessment, business inventory, marketing, fnance, medicine, etc. This is a frst curse in probability
theory that is designed to prepare you to develop basic probabilistic models ±or describing and
studying uncertain or random phenomena and to make better predictions and decisions. By the
end o± this curse, students should
1. possess an adequate background and understanding o± basic concepts in probability theory;
1
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document3. be proFcient in the calculusbased mathematical skills and tools needed to solve problems on
basic topics in probability theory.
Course objectives
1. Basic Probability.
• Describe the sample space of an experiment using set notation.
• ±ind the probabilities of complements, unions, and intersections of events.
• Use counting tools to enumerate the number of equally likely outcomes of simple exper
iments.
• Use the law of total probability and Bayes’ Rule to calculate probabilities.
• DeFne and identify independence of events.
• Calculate conditional probabilities of events.
2. Random Variables.
This is the end of the preview. Sign up
to
access the rest of the document.
 Spring '10
 scott
 Data Structures

Click to edit the document details