Provides a strong foundation in probability and an introduction to statistics and machine learning. Includes experience with translating engineering problems into probabilistic models, and working with these models analytically and algorithmically. Prepares students for upper-level electives that use probabilistic reasoning.
HW7
In this problem, we will use the correlation coefficient to discover linear relationships in a syn- thetic data set.
HW8
How can we use probability and machine learning to automate expert decisions, such as breast cancer diagnosis? In this problem, we will continue to examine (part of) the Wisconsin Breast Cancer Diagnostic Dataset created by W. H. Hoberg, W. N. Street, and O. L. Mangasarian and hosted by the UCI Machine Learning Respository.
HW9
In this problem, we will apply the LLSE estimation framework to predict the compressive strength of concrete. Specifically, we will use the Concrete Compressive Strength Data Set created by I.-C. Yeh (initially for the paper) and hosted by the UCI Machine Learning Repository.
HW10 (Cats and Dogs Images Classificaiton Challenge)
We will explore some basic machine learning concepts through an image classification case study. Specifically, we will write code for a machine learning pipeline that determines whether a 64 × 64 grayscale image is a picture of a cat or a dog.