2021-2022 Academic Catalog 
    Jul 05, 2022  
2021-2022 Academic Catalog

DSCI 408 - Machine Learning

Credits: 3
This is an introductory course in machine learning intended primarily for students majoring or minoring in Mathematics, Data Science, or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics, or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills, and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, k-means clustering, error calculation, optimization training, tree-based algorithms (including random forests), frequent item sets andrecommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks).
Related Courses: DSCI-508
Prerequisite: DSCI-302 or DSCI-303