— Probability & Statistical Modeling for Computer Science
This version has ended; see updated course, below. Course Description: Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics.
Prerequisites & course information
This version has ended; see updated course, below. Course Description: Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One open to Computer Science and Computer Science Engineering Majors only. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). ECS 132 — Probability & Statistical Modeling for Computer Science (4 units) Course Description: Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics. Prerequisite(s): (ECS 040 or ECS 034 or ECS 036B ); ECS 020 ; MAT 021C ; ( MAT 022A or MAT 027A or MAT 067 ). Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One open to Computer Science and Computer Science Engineering Majors only. Grade Mode: Letter. General Education: Science & Engineering (SE). This course version is effective from, and including: Winter Quarter 2027. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One open to Computer Science and Computer Science Engineering Majors only. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). ECS 132 — Probability & Statistical Modeling for Computer Science (4 units) Course Description: Univariate and multivariate distributions. Estimation and model building. Markov/Hidden Markov models. Applications to data mining, networks, security, software engineering and bioinformatics. Prerequisite(s): (ECS 040 or ECS 034 or ECS 036B ); ECS 020 ; MAT 021C ; ( MAT 022A or MAT 027A or MAT 067 ). Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One open to Computer Science and Computer Science Engineering Majors only. Grade Mode: Letter. General Education: Science & Engineering (SE). This course version is effective from, and including: Winter Quarter 2027.
Sources
- Course catalogue ↗2026-2027