MAT 233 - Finite Mathematics for Computer Technologies

3 credit hours - Three hours weekly; one term.
This course meets the Mathematics General Education Requirement

Focus on mathematical topics that are useful in the information sciences. Learn basic linear algebra and its applications in solving a large system of linear equations; game theory; Leontief models of industrial inputs and outputs; the Simplex method; probability; combinatorics; decision theory; and Markov chains. Study topics such as random variables and distributions, Bernoulli trials, normal distribution, or difference equations.

Prerequisite(s): MAT 191 or MAT 230 .

Note: Credit is not given for both MAT 133 and MAT 233. Typically offered at MC and OL; all terms.

Course Outcomes:

Solve applications involving matrices by using the Gauss-Jordan method, inverse matrices and matrix operations.
- Use matrices to solve systems of linear equations.
- Use matrix operations to solve industrial input-output models.
- Find the expected value of a game for mixed strategies.
- Use data to create Game Theory and input-output models.
Determine linear independence of vectors.
- Solve a vector equation.
- Select a subset of linearly independent vectors.
- Find the rank of a matrix.
Solve linear programming problems using the graphical and Simplex approaches.
- Identify feasible region of linear constraints.
- Compare a system of linear inequalities with a system of linear equations.
- Create a Simplex model.
- Develop models to optimize objective functions under sets of linear constraints.
Use set theory and the principles of counting and probability concepts to calculate probabilities.
- Use Venn diagrams to find the cardinality of compound sets.
- Describe a sample space and its probability distribution.
- Use mathematical rules to count arrangements of objects.
- Apply different probability models to different events, including mutually exclusive and independent events.
- Solve Bayes’ Theorem models.
Apply Markov processes to the solution of probability problems.
- Synthesize the concepts of probability and matrices.
- Predict a distribution vector after a finite number of time steps.
- Calculate and interpret a steady-state distribution vector.
- Use Markov systems to model and solve steady-state problems.
Use difference equations to model interest and finance applications or perform basic statistical analysis.
- Use difference equations to model interest and finance applications or perform basic statistical analysis.
- Recognize a random variable and decide its values.
- Recognize a binomial random variable and create its probability distribution.
- Organize, analyze and interpret numerical data.