Learning Outcomes What you will know

This page collects all of the “outcomes” associated with individual modules. Outcomes identify what students will know and be able to do if they master the material.

Understand how to succeed in EE 342

You get familiar with the Moreal framework, which enables moduled-based learning.
You understand the goals and grading policies of EE 342.
You share with the class something about yourself.
You get familiar with the first preliminary concept in this course – sets.

Referencing modules: Introduction to ECE 342

Probabilistic models and probability laws

You understand the definition and key elements of a probabilistic model.
You understand the probability laws.
You can formulate a probabilistic model from a problem description.
You can compute the probability of an event.

Referencing modules: Probabilistic Models

Conditional probability and Bayes' rule

You understand the intuition and the definition of conditional probability.
You understand the multiplication rule and know how to use it in tricky scenarios.
You understand the total probability theorem and know how to use it in tricky scenarios.
You understand the Bayes’ rule and know how to use it for reasoning.

Referencing modules: Conditional Probability and Bayes' Rule

Independence

You understand the definition of independent events.
You understand the definition of conditionally independent events.
You understand the difference between pairwise independence and independence of more than two events.

Referencing modules: Independent Events

Discrete random variables

You understand the distinction between a random outcome and a random variable.
You understand the meaning of probability mass function (PMF).
You understand Bernoulli, binomial, geometric and Poisson random variables and their PMFs.
You can recognize a random variable as one of the special discrete random variables.
You know how to calculate the mean and variance of a discrete random variable.

Referencing modules: Discrete Random Variables

Multiple Random Variables

You understand joint, marginal, and conditional PMFs.
You know how to compute marginal PMFs from conditional PMFs (similar to total probability theorem).
You understand the concept of conditional expectation.
You know how to compute expectation from conditional expectation using total expectation theorem.
You understand the concept of independent random variables.
You know the properties of expectations and variances of independent random variables.

Referencing modules: Multiple Random Variables

Continuous Random Variables

You understand the concept of PDFs of continuous random variables and its difference from PMFs of discrete random variables.
You know how to calculate the expectation and the variance from the PDF.
You understand the concept of cumulative distribution functions (CDFs).
You know the relationship between PDFs and CDFs.
You know how to calculate the PDF from the CDF and calculate the CDF from the PDF.

Referencing modules: Continuous Random Variables

Central Limit Theorem

You understand the properties of normal/Gaussian random variables.
You know how to convert an arbitrary normal random variable to a standard normal random variable.
You understand the Central Limit Theorem.
You know how to use normal approximation to calculate probabilities.

Referencing modules: Central Limit Theorem

Markov Chains

You understand Understand the definition of a Markov chain, the Markov property, and the stationary property.
You know how to draw the transition probability graph and write the transition probability matrix.
You understand recurrent and transient states in a Markov chain.
You know how to compute the steady-state distribution of a Markov chain.

Referencing modules: Markov Chains