MATH 3215 - Probability and Statistics
From Georgia Tech Student Wiki
MATH 3215 covers basic probability, the statistical method, expectation and variance, the law of large numbers and central limit theorem, and hypothesis testing and confidence intervals.
It is a 3 credit-hour course intended for advanced undergraduates and an introduction for graduate students.
Topic List[edit | edit source]
- Combinatorics and discrete probability.
- Conditional probability, Bayes' theorem, and independence.
- Random variables.
- Hypothesis testing.
- Expectation and variance.
- Chebyshev's theorem and the law of large numbers.
- Continuous distributions.
- Joint and marginal distributions.
- Important families of distributions (Poisson, binomial, normal).
- Normal approximation and the central limit theorem.
- Parameter estimation.
- Confidence intervals.
- Chi-square and goodness of fit.
Class Structure[edit | edit source]
Lecture, homework, exam, and sometimes a project in machine learning toward the end of the semester.
Prerequisite Knowledge[edit | edit source]
Multivariable calculus.
Equivalent Courses[edit | edit source]
None
Resources[edit | edit source]
There is no textbook required by the department.
But at least two different professors have taught this while either recommending or requiring Probability and Statistical Inference by Hogg and Tannis.
Also both professors have recommended the OpenCourseware MIT class 18.05.
