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MTH 304
Probability
Topics include: Elements of Probability Theory. Discrete Probability Distribution
(Hyper-geometric, Binomial, Poisson). Normal Distribution and its applications.
Lognormal Distribution, Multivariate Distributions, Covariance and Correlation, Moment
Generating Functions, Central limit theorem and applications. Computer programming is an essential part of this course. Students will understand the concepts of this course both theoretically and through the use of computers.
Weekly Contact: Lab: 1 hr. Lecture: 3 hrs.
GPA Weight: 1.00
Course Count: 1.00
Billing Units: 1
Mentioned in the Following Calendar Pages
*List may not include courses that are on a common table shared between programs.
- CPS 521 - Introduction to Data Science
- CPS 707 - Software Verification and Validation
- CPS 721 - Artificial Intelligence I
- CPS 803 - Machine Learning
- CPS 824 - Reinforcement Learning
- CPS 843 - Introduction to Computer Vision
- CPS 844 - Data Mining
- CPS 870 - Applied Natural Language Processing
- Computer Science Core Elective Table I
- ECN 129 - Statistics for Economics I
- Experiential Learning
- Financial Mathematics
- MTH 380 - Probability and Statistics I
- MTH 404 - Statistics
- MTH 410 - Statistics
- MTH 500 - Introduction to Stochastic Processes
- MTH 642 - Data Analytics: Advanced Methods
- MTH 655 - Machine Learning in Business
- MTH 665 - Mathematical Game Theory
- MTH 707 - Modelling and Searching Networks
- Mathematics and Its Applications
- PCS 335 - Thermodynamics and Statistical Physics
- PCS 352 - Nuclear Physics/Radiation Protection
- PCS 810 - Complex Networks and Applications
