BAYESIAN MACHINE LEARNING FOR MARKETING

Name: BAYESIAN MACHINE LEARNING FOR MARKETING
Availability: OutOfStock

MARKETNG 779

과목 설명

Learn to resolve common marketing tasks such as consumer choice prediction, market analysis, and product development by using Bayesian statistical learning methods. A broad range of methods are covered, from standard approaches such as hierarchical modeling to more advanced techniques, including Bayesian Optimization and Bayesian Deep Learning.

선수과목

Graduate/professional standing and (GENBUS 656 , ECON 704 , ECON 709 , STAT 609 , or MATH/STAT 709 ) or declared in graduate Business Exchange program

충족 요건

This course does not satisfy any prerequisites.

학점

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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

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

MARKETING MANAGEMENT

MARKETNG 700

Provides a framework for evaluating marketing problems and developing a marketing strategy. Customer, competitor, and collaborator factors are emphasized as foundations for marketing decision-making. Examines the key aspects of product, pricing, distribution, and promotion strategy.

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.

MARKETING ANALYTICS

MARKETNG 815

Study of the impact of analytics on successful marketing decisions. A spectrum of topics include Marketing Metrics, Digital Analytics, Marketing Response Models, Segmentation, Product Design, Experimentation and Big Data. Applicable to careers in marketing analytics, product management and consulting.

BAYESIAN STATISTICS

ECON/GENBUS/STAT 775

Introduces the theory, methods, and computational procedures needed to perform advanced Bayesian data analyses. Predictive and decision-theoretic motivations including subjective probability, risk, admissibility, and exchangeability; highlights key components of Bayesian analysis (i.e., prior, likelihood, posterior, and predictive distributions) within standard parametric models and advanced hierarchical and multilevel models; demonstrates the iterative process of model specification, implementation, criticism, and revision with applied case studies; implements computational techniques (e.g., Markov chain Monte Carlo, variational inference) in modern probabilistic programming languages.

MARKETING ANALYTICS

MARKETNG 450

Impact of analytics on successful marketing decisions. Topics include marketing metrics, digital analytics, marketing response models, segmentation, product design, experimentation and big data. It is designed for students with some background in quantitative methods and an exposure to basic marketing research concepts. A combination of lectures, cases and hands-on model building focused on marketing analytics.