CSE 103A Practical Introduction to Probability and Statistics
Distributions over the real line. Independence, expectation, conditional expectation, mean, variance. Hypothesis testing. Learning classifiers. Distributions over R^n, covariance matrix. Binomial, Poisson distributions. Chernoff bound. Entropy. Compression. Arithmetic coding. Maximal likelihood estimation. Bayesian estimation. CSE 103 is not duplicate credit for ECE 109, ECON 120A, or MATH 183.
Prerequisites
From the catalog: “MATH 20B and CSE 21 or MATH 154 or MATH 158 or MATH 184 or MATH 188; restricted to CS25, CS26, CS27, CS28, and CS29 majors. Other students will be allowed as space permits.”
Where it sits in degree plans
Suggested plans most often put it in Year 2 · Winter (1 program).
One of the options in (2)
- Artificial Intelligence
- Computer Science (B.S.)
Courses that list CSE 103 as a prerequisite (11)
- CSE 150A Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision-Making
- CSE 150B Introduction to Artificial Intelligence: Search and Reasoning
- CSE 151A Machine Learning: Learning Algorithms
- CSE 151B Deep Learning
- CSE 153 Machine Learning for Music
- CSE 153R Machine Learning for Music
- CSE 156 Statistical Natural Language Processing
- CSE 158 Recommender Systems and Web Mining
- CSE 158R Recommender Systems and Web Mining
- DSC 148 Introduction to Data Mining
- MATH 189 Exploratory Data Analysis and Inference