Learning Outcomes

By the end of the course, you will be able to:

• Discuss the different forms of a linear equation
• Arrange a system of linear equations into augmented matrix form
• Solve a system of linear equations using REF or R-REF
• Define the idea of a vector space and name the two vector spaces introduced during the course
• Explain how linear independence and span are related
• Compute a least-squares solution via pseudoinverse
• Recall the three elements of a probability model
• Distinguish between a PMF and PDF
• Compare covariance and variance
• Contrast the law of large numbers and the central limit theorem
• Describe the steps involved in computing a joint PDF
• Indicate how to find the mean squared error using the Bayesian estimator