Readings "Passive" learning opportunities

This page collects together all of the “readings” associated with individual modules.

In this site, readings represent “passive” learning opportunities, as opposed to experiences, which represent “active” learning opportunities. In many courses, readings and experiences together constitute the “assignments”.

Module: Introduction to ECE 342

EE 342 Syllabus

Basic information about the class

Brief Overview

Very brief overview

Sets and Algebra of Sets

Sets and algebra of sets

Module: Probabilistic Models

Probabilistic Models

Rigorously define a probabilistic model

Probability Laws

Law & order in the probabilistic world

Module: Conditional Probability and Bayes' Rule

Conditional Probability

Conditioning improves your chance.

Total Probability Theorem

Divide and conquer.

Bayes' Rule

The first well-known result we learn.

Module: Independent Events

Independence of Random Events

Independence of random events

Module: Discrete Random Variables

Discrete Random Variables

PMF, special discrete random variables

Expectation and Variance

Expectation and variance

Module: Multiple Random Variables

Multiple Random Variables

Joint and marginal PMFs

Conditioning

Conditional PMF, conditional expectation

Independence of Random Variables

Independent random variables

Module: Continuous Random Variables

Continuous Random Variables

Continuous random variables

Cumulative Density Function

Cumulative density function

Module: Central Limit Theorem

Normal Random Variables

Normal/Gaussian random variables

Central Limit Theorem

Central limit theorem

Module: Markov Chains

Markov Chains

Markov chain

Steady-State Behavior

Steady-state / stationary distribution