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