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DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION

MATH 616
과목 설명

An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.

선수과목

(MATH 320 , MATH 340 , MATH 341 or MATH 375 ) and (MATH/STAT 309 , MATH/STAT 431 , STAT 311 or MATH 531 ) and (MATH 322 , MATH 341 , MATH 375 , MATH 421 , or MATH 467 ), graduate/professional standing, or declared in Mathematics VISP

충족 요건

This course does not satisfy any prerequisites.

학점

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미보고

평점
3.57

과거 데이터 대비 변화 없음

수료율
100%

과거 데이터 대비 변화 없음

A 비율
40.54%

과거 데이터 대비 변화 없음

학급 규모
37

과거 데이터 대비 변화 없음

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

APPLIED TIME SERIES ANALYSIS, FORECASTING AND CONTROL I

STAT 701

Theory and application of discrete time series models illustrated with forecasting problems. Principles of iterative model building. Representation of dynamic relations by difference equations. Autoregressive integrated Moving Average models. Identification, fitting, diagnostic checking of models. Seasonal model application to forecasting in business, economics, ecology, and engineering used at each stage, which the student analyzes using computer programs which have been specially written and extensively tested.

STOCHASTIC COMPUTATIONAL METHODS

MATH 717

Introduction to computational methods that use stochastic algorithms and/or methods that are applied to random or stochastic mathematical problems. The main emphasis will be placed on learning practical tools, while some aspects of theoretical foundations will also be covered (e.g., basic error analysis for numerical solution of stochastic differential equations (SDEs), and basic convergence of Monte Carlo methods). Topics include Monte Carlo methods, Bayesian inference and Bayesian sampling, simulation of Markov chains, numerical analysis for SDEs, data assimilation / state estimation, stochastic optimization methods and random sketching. Applications to science, engineering, finance, data science, and other practical problems also included.

APPLIED DYNAMICAL SYSTEMS, CHAOS AND MODELING

MATH 415

An introduction to nonlinear dynamical systems including stability, bifurcations and chaos. The course will give underlying mathematical ideas, but emphasize applications from many scientific fields.

MATHEMATICAL METHODS IN DATA SCIENCE

MATH 535

A rigorous introduction to mathematical concepts important for modern data science. Topics include: matrix factorizations, optimization theory and algorithms, probabilistic models, finite Markov chains. Mathematical techniques are motivated by and illustrated on a range of applied problems from machine learning and statistics.

DYNAMIC SYSTEMS

ME 340

Mathematical modeling and analysis of dynamic systems with mechanical, thermal, and fluid elements. Topics: time domain solutions, analog computer simulation, linearization techniques, block diagram representation, numerical methods and frequency domain solutions. Students are assumed to have basic competence in particle and planar rigid body dynamics, matrix and vector algebra, and linear differential equations.