C&EE 110 · CEE 110Introduction to Probability and Statistics for Engineers
Civil and Environmental Engineering · 4 units · Undergraduate upper division (100-199)
Recommended: course M20. Introduction to fundamental concepts and applications of probability and statistics in civil engineering, with focus on how these concepts are used in experimental design and sampling, data analysis, risk and reliability analysis, and project design under uncertainty. Topics include basic probability concepts, random variables and analytical probability distributions, functions of random variables, estimating parameters from observational data, regression, hypothesis testing, and Bayesian concepts.
Letter grading.
When it runs
Checking the Schedule of Classes…
Requisites
Official UCLA wording
Requisites: Mathematics 32A, 33A.
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
C&EE 110
- CH ENGR M148Introduction to Data Science
- COM SCI 112Modeling Uncertainty in Information Systems
- COM SCI 212AQueueing Systems Theory
- COM SCI 218Advanced Computer Networks
- COM SCI 246Web Information Management
- COM SCI 262ALearning and Reasoning with Bayesian Networks
- COM SCI 262ZCurrent Topics in Cognitive Systems
- STATS M241Current Topics in Causal Modeling, Inference, and Reasoning
- EC ENGR M119Fundamentals of Embedded Networked Systems
- COM SCI M119Fundamentals of Embedded Networked Systems
- EC ENGR M146Introduction to Machine Learning
- STATS C163Generative Data Science
- EC ENGR C147ANeural Networks and Deep Learning
- EC ENGR C147BNeural Networks and Deep Learning II
- EC ENGR 201CArtificial Intelligence on Chip
- EC ENGR C247ANeural Networks and Deep Learning
- EC ENGR C247BNeural Networks and Deep Learning II
- COM SCI C121Probabilistic Models in Computational Genomics
- EC ENGR M148Introduction to Data Science
- COM SCI C122Algorithms in Computational Genomics
- COM SCI C124Machine Learning Applications in Genetics
- COM SCI 163Deep Learning for Computer Vision
- COM SCI M146Introduction to Machine Learning
- COM SCI C160FFoundation Models: Principles and Practice
- COM SCI 162Natural Language Processing
- COM SCI 247Advanced Data Mining
- COM SCI C260FFoundation Models: Principles and Practice
- COM SCI 261Deep Generative Models
- COM SCI M148Introduction to Data Science
- COM SCI 168Computational Methods for Medical Imaging
- COM SCI C221Probabilistic Models in Computational Genomics
- COM SCI C222Algorithms in Computational Genomics
- COM SCI C224Machine Learning Applications in Genetics
15 courses list this as a requisite. The whole downstream is here — 37 courses over 3 levels. Every course here opens its own tree.