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RISK ANALYTICS

ACTSCI 657
과목 설명

Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications. Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models. Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.

선수과목

(ACTSCI 640 , GENBUS 656 , STAT 333 , or STAT 340 ), or declared in undergraduate Business Exchange program

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.6

1.62% 과거 데이터 대비

수료율
100%

과거 데이터 대비 변화 없음

A 비율
47.06%

20.78% 과거 데이터 대비

학급 규모
34

32.47% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

RISK ANALYTICS AND BEHAVIORAL SCIENCE

RMI 660

Learning to effectively navigate uncertainty is a key issue for all areas of business and especially for those engaged in explicit risk-management activities. The skills for success in these environments include knowing how to formulate the right questions to ask, how to identify the information that is valuable for answering those questions, understanding how to process incoming information to distinguish signal from noise, and how to use that information to put structure to uncertain environments so that one can take good calculated risks. This collective set of skills are at the heart of "decision analysis", which is a structured rational approach to making decisions under uncertainty.

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.

FUNDAMENTALS OF SHORT-TERM ACTUARIAL MODELING

ACTSCI 652

Definition and selection of probability distributions appropriate for insurance data that are heavily tailed and skewed.

MATHEMATICAL FOUNDATIONS OF BUSINESS ANALYTICS

GENBUS 317

Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications and will be used to analyze business data and make inferences and predictions.

ACTUARIAL STATISTICS FOR RISK MODELING

ACTSCI 640

Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.