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Cmpe 466 computer graphics. 3D geometric transformations. (Сhapter 9)
Modeling non-stationary variables
Wide-angle X-ray scattering (WAXS) geometries. In-plane diffraction geometry
Forecasting with bayesian techniques MP
Discrete Mathematics Sets
Theory without practice is empty, practice without theory is blind
Evolutionary games. (Lecture 7)
Combinational logic design
The travelling salesman problem
Sequential games. (Lecture 4)
Recitation class
Probabilistic Models. Chapter 11
The mean values
N-ary relations and their applications. (Rosen 8.2)
Решение уравнений sinx=a. Понятие арксинуса числа
Simultaneous games. Oligopoly. (Lecture 2)
Object tracking using particle filter
Repeated games. (Lecture 6)
Evolutionary game theory. (Lecture 11)
Chapter 3. Polynomial and Rational Functions. 3.1 Quadratic Functions
Chapter 3. Polynomial and Rational Functions. 3.2 Polynomial Functions and Their Graphs
Chapter 3. Polynomial and Rational Functions. 3.4 Zeros of Polynomial Functions
Geometric Modeling - Parametric Representation of Synthetic Curves
Solving linear recurrence relations
Discrete random variables – expected variance and standard deviation. Discrete Probability Distributions. Week 7 (1)
Correlation and Regression
Ryspekov’s Fibonacci sequence formula Global Revival
Sequential games. Empirical evidence and bargaining. (Lecture 5)
Descriptive statistics. Elementary statistics. Larson. Farber. (Chapter 2)
Statistics. Data Description. Data Summarization. Numerical Measures of the Data
Mechanism design. (Lecture 9)
The Aerodynamics Of A Single-Blade Rotor
Drawing triangles
Mathematics for Computing. Lecture 2: Logarithms and indices
Mathematics for Computing 2016-2017. Lecture 1: Course Introduction and Numerical Representation
ორი სიბრტყის თანაკვეთა
Quiz 1
Graph theory irina prosvirnina. Definitions and examples. Paths and cycles
Calculating the probability of a continuous random variable – Normal Distribution. Week 9 (1)
Parametric Linear Programming
Functions and graphs. Chapter 2. Combinations of functions; composite functions
Digital Image Stabilization
Elements of probability. (Lecture 3)
Confidence interval and Hypothesis testing for population mean (µ) when is known and n (large)
Correlation and regression
Index of Refraction
Displaying data – shape of distributions. Week 3 (1)
Measures of variation. Week 4 (2)
Probability Concepts
Basics of functions and their graphs
Methods of proof
Communication and signaling. (Lecture 8)
Auctions. (Lecture 10)
Discrete mathematics. Probability
Mixed strategy Nash equilibrium. (Lecture 3)
Time series models. Static models and models with lags
The binomial model for option pricing
Chapter 1. Polynomial and Rational Functions. 3.3. Dividing Polynomials; Remainder and Factor Theorems
Ryspekov’s Fibonacci sequence formula
Time quiz
Types of Data – (continued). Week 2 (2)
Using numerical measures to describe data. Measures of the center. Week 3 (2)
Measures of variation. Week 4 (1)
Random variables – discrete random variables. Week 6 (2)
An over view of statistics
Introduction to normal distributions
Collision Detection
Arithmetic fundamentals of number systems
Derivatives of Products and Quotients
Common Probability Distributions
Probability theory. Probability Distributions Statistical Entropy
Descriptive statistics. Frequency distributions and their graphs. (Section 2.1)
The Taylor Formula
Mathematical Induction
Math end algebra. Vector
Trigonometriýa
Intro to Geometric Modeling (GM)
Mathematics for еconomists. (Week 1-12)
Introductory statistics
Постороение сечений
Sampling in quantitative research
Panel.Methods
Introduction to Statistics. Week 1 (2)
Introduction to Statistics. Week 1 (1)
Types of Data – categorical data. Week 2 (1)
Empirical rule - Probabilities. Week 5 (1)
Conditional Probabilities Statistical Independence. Week 6 (1)
George Boole
Descriptive statistics
Matrix Equations and Systems of Linear Equations
Random variables
Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication
Mathematical Induction
Scalars, vectors and tensors
Hypothesis testing
Economics of pricing and decision making. (Lecture 1)
Russian mathematician. Sofia Kovalevskaya
Solid Modeling
Rescaling, sum and difference of random variables. (Lecture 4)
Source coordinate definition by. Non-destructive assay
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