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Cmpe 466 computer graphics. 3D geometric transformations. (Сhapter 9)
Modeling non-stationary variables
Numbers 11-20
Matrices - Introduction. Lecture 1-3
Hypothesis Testing with Two Samples
Probabilistic Models. Chapter 11
The mean values
Correlation and Regression
Geometric Modeling - Parametric Representation of Synthetic Curves
Determinants
Confidence interval estimation
Discrete mathematics. Sets
Correlation and regression
Boolean algebra. Logic operations. Formula and their conversion
Forecasting with bayesian techniques MP
Confidence interval and Hypothesis testing for population mean (µ) when is known and n (large)
Solution Methods Applied Computational Fluid Dynamics. Lecture 5
Descriptive statistics
Statistics. Data Description. Data Summarization. Numerical Measures of the Data
Inverse of a Square Matrix
Descriptive statistics. Frequency distributions and their graphs. (Section 2.1)
N-ary relations and their applications. (Rosen 8.2)
Exploring Assumptions Normality and Homogeneity of Variance
Discrete Mathematics Sets
Combinational logic design
Exponentials and logarithms. Lecture 2.1
Normal Probability Distributions
Wide-angle X-ray scattering (WAXS) geometries. In-plane diffraction geometry
Calculating the probability of a continuous random variable – Normal Distribution. Week 9 (1)
Hypothesis testing
Descriptive statistics. Elementary statistics. Larson. Farber. (Chapter 2)
4.2 Introduction to Limits
Discrete random variables – expected variance and standard deviation. Discrete Probability Distributions. Week 7 (1)
Random variables – discrete random variables. Week 6 (2)
Chapter 3. Polynomial and Rational Functions. 3.4 Zeros of Polynomial Functions
Index of Refraction
Chapter 3. Polynomial and Rational Functions. 3.2 Polynomial Functions and Their Graphs
Binary Variables
Using first derivative. Using second derivative
Introduction to normal distributions
Sequential games. (Lecture 4)
The travelling salesman problem
Mixed strategy Nash equilibrium. (Lecture 3)
Modelling with exponentials and logarithms
Modelling with Exponentials and Logarithms
Theory without practice is empty, practice without theory is blind
Scalars, vectors and tensors
Math end algebra. Vector
Basic arithmetic
Measures of variation. Week 4 (2)
Stolz-Cesaro Theorem
Object tracking using particle filter
Evolutionary games. (Lecture 7)
Sampling in quantitative research
Discrete Probability Distributions: Binomial and Poisson Distribution. Week 7 (2)
Types of Data – (continued). Week 2 (2)
Сумма и разность синусов и косинусов
CENG 789 – Digital Geometry Processing
Probability theory. Probability Distributions Statistical Entropy
Matrix Equations and Systems of Linear Equations
Auctions. (Lecture 10)
Chapter 1. Polynomial and Rational Functions. 3.3. Dividing Polynomials; Remainder and Factor Theorems
Using numerical measures to describe data. Measures of the center. Week 3 (2)
Differentiation. A “derivative”,
Discrete mathematics. Probability
Measures of variation. Week 4 (1)
Recitation class
Economics of pricing and decision making. (Lecture 1)
Time quiz
Chapter 3. Polynomial and Rational Functions. 3.1 Quadratic Functions
Trigonometry 2
Trigonometry 3
Trigonometry 1
Differentiation. Sketching functions
Simultaneous games. Oligopoly. (Lecture 2)
Ch8: Hypothesis Testing (2 Samples)
Method of undetermined coefficients
Propositional logic
Probability Random Variables Preparatory Notes
Sequential games. Empirical evidence and bargaining. (Lecture 5)
Matrices: Basic Operations
Arithmetic fundamentals of number systems
Types of Data – categorical data. Week 2 (1)
Correlation Regression
Introductory Statistics 1. AP Statistics
Random variables
Graph transformation 1
Foundation Year Program NUFYP Mathematics
3.5 Trigonometry 5
Expert judgment method. (Lecture 1-4)
Trigonometriýa
Repeated games. (Lecture 6)
Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication
Introduction to Statistics
Mathematics for Computing. Lecture 2: Logarithms and indices
Empirical rule - Probabilities. Week 5 (1)
Displaying data – shape of distributions. Week 3 (1)
Matlab Linear Programming
Practical application of the method of Kona-Shema. Pseudopotentials method
Communication and signaling. (Lecture 8)
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