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Courseware
UCI Math 2A is the first quarter in Single-Variable Calculus and covers the following topics: Introduction to derivatives, calculation of derivatives of algebraic and trigonometric functions; applications including curve sketching, related rates, and optimization. Exponential and logarithm functions.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Calculus
- Resource Type:
- Courseware
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Courseware
This Pre-Calculus course is designed to prepare students for a calculus course. This course is taught so that students will acquire a solid foundation in algebra and trigonometry. The course concentrates on the various functions that are important to the study of the calculus.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Trigonometry Precalculus Algebra
- Resource Type:
- Courseware
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Courseware
After reviewing tools from probability, statistics, and elementary differential and partial differential equations, concepts such as hedging, arbitrage, Puts, Calls, the design of portfolios, the derivation and solution of the Blac-Scholes, and other equations are discussed.
- Subjects:
- Finance and Mathematics and Statistics
- Keywords:
- Business mathematics
- Resource Type:
- Courseware
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Courseware
Second introductory course covering basic principles of probability and statistical inference. Topics: Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Probabilities Mathematical statistics
- Resource Type:
- Courseware
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Courseware
Introductory course covering basic principles of probability and statistical inference. Topics covered in this course: Axiomatic definition of probability, random variables, probability distributions, expectation.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Probabilities Mathematical statistics
- Resource Type:
- Courseware
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Courseware
This course is intended for both mathematics and biology undergrads with a basic mathematics background, and consists of an introduction to modeling biological problems using continuous ODE methods (rather than discrete methods as used in 113A). We describe the basic qualitative behavior of dynamical systems in the context of a simple population model and, as time allows, introduce other types of models such as chemical reactions inside the cell or excitable systems leading to oscillations and neuronal signals. Certain topics from linear algebra that are needed for this course are presented as well, so a linear algebra prerequisite is not necessary.
- Subjects:
- Mathematics and Statistics and Biology
- Keywords:
- Biology -- Mathematical models
- Resource Type:
- Courseware
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Courseware
This course is an introduction to principles and techniques of visual communication, and provides opportunities for science and engineering majors to acquire practical skills in the visual computer arts, in a studio environment. Students will learn how to create graphics for print and web, animations, and interactive media, and how to use these techniques to effectively communicate scientific and engineering concepts for learning and teaching. This class involves three hands-on creative projects, which will be presented in class.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Visualisation
- Keywords:
- Information visualization
- Resource Type:
- Courseware
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Courseware
This course provides a rigorous treatment of non-cooperative solution concepts in game theory, including rationalizability and Nash, sequential, and stable equilibria. It covers topics such as epistemic foundations, higher order beliefs, bargaining, repeated games, reputation, supermodular games, and global games. It also introduces cooperative solution concepts—Nash bargaining solution, core, Shapley value—and develops corresponding non-cooperative foundations.
- Subjects:
- Mathematics and Statistics and Economics
- Keywords:
- Game theory
- Resource Type:
- Courseware
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Courseware
Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.
- Subjects:
- Mathematics and Statistics and Biology
- Keywords:
- Cognitive science Statistics
- Resource Type:
- Courseware
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Courseware
With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Biology
- Keywords:
- Genomes Genomics
- Resource Type:
- Courseware