EK381: Probability, Statistics, and Data Science for Engineers

The materials for EK381 were created by Professors David Castanon and Bobak Nazer at Boston University.

Motivation

It is becoming increasingly clear that the next generation of engineers will need to draw upon concepts and techniques from probability and statistics to tackle the challenges posed by uncertain, complex systems and large, high-dimensional data sets. This course is intended to give all engineering students a strong foundation in probability and statistics as well as an introduction to ideas from data analytics and machine learning. Any student that successfully completes this course will be well-prepared to take upper-electives in machine learning, data analytics, random processes, as well as any other course that draws heavily upon probabilistic reasoning.

Concept Videos

This is a “flipped” class meaning that, prior to each lecture, students watch concept videos that introduce the main concepts and work through some basic examples. Lectures will be devoted towards working out more complex examples, developing high-level intuition, connecting concepts to engineering applications, and tying the math to computation and real datasets.

Exploring Data

One of the most important modern applications of probability is machine learning. For the EK381 capstone project, you will create a machine learning system for classifying cat and dog images. The dataset and supporting MATLAB and Python code will be available on this website soon.