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ESTIMATION AND DECISION THEORY

ECE 830
과목 설명

Estimation and decision theory applied to random processes and signals in noise: Bayesian, maximum likelihood, and least squares estimation; the Kalman filter; maximum likelihood and maximum aposteriori detection; adaptive receivers for channels with unknown parameters or dispersive, fading characteristics; the RAKE receiver; detection systems with learning features.

선수과목
충족 요건
학점

미보고

개설 시기

미보고

평점
3.71

12.55% 과거 데이터 대비

수료율
100%

2.15% 과거 데이터 대비

A 비율
71.43%

105.63% 과거 데이터 대비

학급 규모
7

-55.79% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO DECISION ANALYSIS

ISYE 516

Overview of modeling techniques and methods used in decision analysis, including multiattribute utility models, decision trees, and Bayesian models. Psychological components of decision making are discussed. Elicitation techniques for model building are emphasized. Practical applications through real world model building are described and conducted.

PROBABILITY AND RANDOM PROCESSES

ECE 730

Review of basic probability. Advanced probability concepts. Random vectors; linear filtering of random processes; stationarity; power spectral densities; estimation; convergence; Markov chains; Poisson process; Wiener process. Knowledge of basic probability analysis [such asE C E 331,STAT/​MATH  431, orSTAT 311] strongly encouraged.

PROBABILITY AND INFORMATION THEORY IN MACHINE LEARNING

COMPSCI/ECE 561

Probabilistic tools for machine learning and analysis of real-world datasets. Introductory topics include classification, regression, probability theory, decision theory and quantifying information with entropy, relative entropy and mutual information. Additional topics include naive Bayes, probabilistic graphical models, discriminant analysis, logistic regression, expectation maximization, source coding and variational inference.

COMMUNICATION SYSTEMS II

ECE 437

Statistical analysis of information transmission systems. Probability of error, design of receivers for digital transmission through additive white Gaussian noise channels and bandlimited channels. Spread spectrum communication systems. Channel capacity, source and error control coding.

INTRODUCTION TO RANDOM SIGNAL ANALYSIS AND STATISTICS

ECE 331

Introduction to probability, random variables, and random processes. Confidence intervals, introduction to experimental design and hypothesis testing. Statistical averages, correlation, and spectral analysis for wide sense stationary processes. Random signals and noise in linear systems.