MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS

Name: MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS
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GENBUS 657

과목 설명

An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.

선수과목

ACTSCI 640 or (GENBUS 656 or concurrent enrollment)

충족 요건

GENBUS 891 and GENBUS 894

학점

미보고

개설 시기

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평점

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변화 계산 불가

수료율

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변화 계산 불가

A 비율

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변화 계산 불가

학급 규모

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과거 데이터 대비 변화 없음

데이터 없음

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS

GENBUS 656

An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.

PRESCRIPTIVE MODELING AND OPTIMIZATION FOR BUSINESS ANALYTICS

GENBUS 730

Introduction to fundamentals of prescriptive analytics with emphasis on business applications. Modeling and mathematical optimization using Excel and Python. Designing, building, testing, and analyzing models, including sensitivity and risk analysis. Developing and solving optimization models, including linear, integer, and nonlinear problems. Course includes some principles of model-building and fundamentals of optimization theory but emphasizes practical application, hands-on learning, and problem-driven exercises.

INTERMEDIATE DATA ANALYSIS FOR BUSINESS

GENBUS 207

Enables you to understand and apply useful approaches to analyzing and presenting data to support business decision making. Emphasis on applications of predictive and prescriptive analytics. Predictive approaches use historical data to infer relationships and forecast future outcomes. Prescriptive methods formulate decision models to identify choices that are optimal with respect to a desired, measurable outcome. Provides experience integrating diverse data sources, modelling uncertainty, and visualizing key insights.

INTRODUCTION TO ARTIFICIAL INTELLIGENCE IN BUSINESS

GENBUS 107

Introduction to the foundational concepts and applications of Artificial Intelligence (AI) in today's business world. Covers what AI is, how it works, and its growing impact on various business contexts. Topics include predictive AI, deep learning, generative AI, leveraging AI tools, and responsible AI.

DATA TO DECISIONS

GENBUS 704

Exploration of statistical inference and data analytics tools. Review of relevant foundations of statistics, machine learning and probability theory. Emphasis on applying the resulting concepts to canonical business examples, using both Excel and R.