INFO-H 515 Statistical Learning
3 credits
- Prerequisites: ECON E570 or HPER T591 or PBHL B561 or PSY 60000 or STAT 51100
- Delivery: On-Campus
Description
This course applies statistical learning methods for data mining and inferential and predictive analytics to informatics-related fields. The course also introduces techniques for exploring and visualizing data, assessing model accuracy, and weighing the merits of different methods for a given real-world application. This course provides an essential toolset for transforming large, complex informatics datasets into actionable knowledge.
Learning Outcomes
- Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
- Explore, transform, and visualize large, complex datasets with graphs in R.
- Solve real-world problems by adapting and applying statistical learning methods to large, complex datasets.
- Identify, assess, and select among statistical learning methods and models for solving a particular real-world problem, weighing their advantages and disadvantages.
- Write programs to perform data analytics on large, complex datasets in R.
- Analyze datasets from case studies in informatics-related fields (e.g., digital media, human-computer interaction, health informatics, bioinformatics, and business intelligence).
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.