## STAT 3320 Probability and Statistics for Computer Scientists Spring 2014 Syllabus

Class Meetings: Friday 9-10 am, in CCSB 3.0404

Office hours: Mondays 4-4:30 pm, Wednesdays 3:30-4:30 pm, Wednesdays 6-6:30 pm, or by appointment,
in CCSB 3.0404.

Prerequisite Course: MATH 1312 (Calculus II) with a grade of C or better.

Textbook (Required): Probability and Statistics for Computer Scientists, by Michael Baron. Published in 2007 by Chapman & Hall CRC, Taylor & Francis Group, Boca Raton, FL. ISBN 1-58488-641-2.

Course Objectives (Learning Outcomes): Students will be able to read a word problem or a corporate report, realize the uncertainty that is involved in a situation described, select a suitable probability model, estimate and test its parameters on the basis of real data, compute probabilities of interesting events and other vital characteristics, and make appropriate conclusions and forecasts (Lallmahomed, M. 2008, Journal of the Royal Statistical Society: Series A, 171 (1), page 312). This course is designed to satisfy the Accreditation Board of Engineers and Technology requirements for probability and statistics.

Activities and Assignments: Students will be expected to attend weekly meetings, read the book, and practice the concepts by solving problems from the textbook assigned for homework. The textbook problems will be supplemented with computer assignments. In-class group activities will be used to encourage active learning. Study groups to discuss the problems and brainstorm approaches to problem solving are encouraged, however, the written assignments and computer programs turned in for grading are to be your own work and reflect your individual effort.

• 25% Exam I
• 25% Exam II
• 25% Cumulative Final Exam
Calculators may not be shared on exams. Cell phone calculators are not permitted on exams.

Letter grades are determined according to the following scale:

• A: 90-100,
• B: 80-89,
• C: 70-79,
• D: 60-69,
• F: <60

Make-up Policy: No make-up late homework are allowed. Make-up examinations will not be given. Students are required to take the two midterms and final exam at the scheduled times.

Attendance Policy: Class attendance is required and noted at the beginning of class; more than two unexcused absences will result in an instructor-initiated drop or final grade reduction.

Academic Integrity Policy: Violations of academic integrity, including unauthorized submission of work performed by others, will be pursued vigorously to result in the most severe sanctions. Please refer to UTEP's policy cited in http://academics.utep.edu/Default.aspx?tabid=23875.

Civility Statement: No text messaging in class. Please silence cell phones before coming to class. Students are expected to actively participate in class discussions and group activities. Group work that is not completed in class is to be finished as homework, so use the class time wisely by staying focused on the class topic and avoiding chit-chat.

Disability Statement: If a student has or suspects she/he has a disability and needs an accommodation, he/she should contact the Disabled Student Services Office (DSSO) at 747-5148 or at or go to Room 106 Union East Building. The student is responsible for presenting to the instructor any DSS accommodation letters and instructions.

Military Statement: If you are a military student with the potential of being called to military service and /or training during the course of the semester, you are encouraged to contact the instructor as soon as possible.

Weekly Schedule (tentative and subject to change):

• Week 1:
• Introduction
• Section 2.1: Definition of Probability; Let's Make a Deal (Monty Hall game)
• Sections 2.1-2.2: Counting Principle for equally likely outcomes; probability rules; independence; system reliability (parallel, series)
• Week 2:
• Section 2.3: How many ways can we select k objects from n available objects (Combinations and Permutations)?
• Section 2.4: Conditional Probability, Law of Total Probability, Bayes Rule
• Week 3:
• Section 3.1: Definition of Random Variable
• Section 3.4: Discrete Random Variables Bernoulli, Binomial; probability mass function
• Week 4:
• Section 3.4: Binomial; Hypergeometric, Geometric, Negative Binomial
• Week 5:
• Section 3.4: Poisson and Poisson approximation of Binomial
• Section 3.3: Expectation and Variance of a Discrete Random Variable; in particular:
• variance as a description of variability
• entropy as the expectation of -ln(p), its importance in estimating the amount of information
• Week 6:
• Section 4.1: Continuous Distributions (density), including joint distributions and joint density
• this topic includes the basics of differentiation and integration of functions of several variables
• Week 7:
• First Midterm Exam (covers topics in Chapters 1-3)
• Section 4.2: density, uniform density
• Week 8:
• Section 4.2: mean and variance of a density; Gaussian density; Exponential and Gamma densities
• Week 9:
• Section 4.3: Central Limit Theorem
• Section 5.2: Simulation of Random Variables
• Week 10:
• Review, Problem solving
• Second Midterm Exam (covers topics in Chapters 4-5)
• Week 11:
• Chapter 8: Statistics and sampling distribution of the sample mean; Statistics and sampling distribution of the sample proportion
• Week 12:
• Chapter 9: Statistical inference; Parameter Estimation (Method of Moments, Maximum Likelihood Method); Confidence Intervals (Pivotal Quantity Method)
• Week 13:
• Chapter 9: Hypothesis Testing; type I and type II errors; anomalous events and how to identify them
• Week 14:
• Chapter 9: Hypothesis Testing (cont-d)
• Week 15:
• Review; Problem solving
• Finals Week:
• A cumulative final exam