HUMAN FACTORS OF DATA SCIENCE AND MACHINE LEARNING

Name: HUMAN FACTORS OF DATA SCIENCE AND MACHINE LEARNING
Availability: OutOfStock
Rating: 4.7 (97 reviews)

ISYE 562

과목 설명

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.

선수과목

(ISYE 210 , ECE 331 , MATH/STAT 310 , STAT 312 , or STAT 340 ), graduate/professional standing, or member of Engineering Guest Students

충족 요건

ECE/ISYE 570

학점

미보고

개설 시기

미보고

평점

3.86

2.22% 과거 데이터 대비

수료율

100%

1.04% 과거 데이터 대비

A 비율

80.95%

12.18% 과거 데이터 대비

학급 규모

-35.05% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTERACTIVE DATA ANALYTICS

ISYE 649

A cognitive engineering approach to human-computer interaction and data visualization in particular. Includes a four-part description of effective visualization: design intent, data and application domain, representation and interface features, and human limits and capabilities. The philosophical perspective, scientific basis, and practical tools for effective data visualization and visual analytics. Data processing and how to create static graphs as well as web-based interactive visualizations using the statistical language R.

FUNDAMENTALS OF INDUSTRIAL DATA ANALYTICS

ISYE 412

Provides an understanding of the fundamentals of using data analytics to make data-driven decisions. Emphasizes applying techniques to industrial engineering problems. Focuses on formulating and solving real industrial problems with the appropriate modeling strategies and analytics principles for better decision making.

PROSEMINAR IN DATA SCIENCE IN HUMAN BEHAVIOR

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.

ENVIRONMENTS AND TOOLS FOR LARGE-SCALE 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.

APPLIED MACHINE LEARNING FOR BEHAVIORAL DATA SCIENCE

PSYCH 752

Introduction to computational approaches in machine learning for the behavioral sciences.