APPLIED MACHINE LEARNING FOR BEHAVIORAL DATA SCIENCE

ISYE 562

An examination of the "human side" of data science. Issues of bias, fairness, trust, and understandability. Unique characteristics of behavioral data, such as representative sampling, human adaptation, and grouped data. Practical skills in behavioral data analytics with a focus on important conceptual, design, and ethical issues specific to behavioral data. Survey of machine learning techniques including supervised learning, unsupervised learning, reinforcement learning, deep learning, and text analysis. Methods are contextualized through engineering case studies.

PSYCH 709

Discussion of current topics in human behavioral data science, including discussion of planned capstone projects, discussion of recent journal articles, training and practice in scientific communication to various stakeholders, and professional development activities.

COMPSCI/ECE 760

Computational approaches to learning: including inductive inference, explanation-based learning, analogical learning, connectionism, and formal models. What it means to learn. Algorithms for learning. Comparison and evaluation of learning algorithms. Cognitive modeling and relevant psychological results.

PSYCH 750

Foundations in programming fundamentals, emphasizing tools and techniques as utilized in human behavioral data science.

PSYCH 755

Provides students with knowledge and experience conducting large-scale behavioral data science projects, independently and in collaboration with others, using a variety of contemporary software tools and environments.