ISE 323 • Midterm • Operations Research II
Eğitmen
Ömer Faruk Altun
Co-founder & Head of Education
2011 yılında Endüstri Mühendisliği okumak için başladığım Sabancı Üniversitesi'nden 2018 yılında Bilgisayar Mühendisi olarak mezun oldum. 13 yıldır Altun ismiyle başta Sabancı Üniversitesi olmak üzere çeşitli okullarda Endüstri ve Bilgisayar Mühendisliği alanlarında ders vermekteyim. Unicourse'ta sunduğum derslerin yanında eğitim departmanının da sorumluluğunu üstlenmekteyim.
1599 TL
Ders Özeti
Paketi Tamamla
🎓 Yeditepe Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
ISE 323 • Midterm
Operations Research II
Ömer Faruk Altun
1599 TL
ISE 323 • Final
Operations Research II
Ömer Faruk Altun
1599 TL
Ders İçeriği
Counting
Basic Principles of Counting
Counting Examples
Permutations
Permutations Example
Groups and Circular Permutation Example
Identical Objects Example 1
Identical Objects Example 2
Identical Objects Example 3
Combination
n choose r
Committee Example 1
Committee Example 2
Ball Example 1
Ball Example 2
Axioms of Probability
Sample Space and Events
Probability
Axioms of Probability
Some Rules
Coin Example
Dice Example
Card Example 1
Card Example 2
Ball Example
Set Example
Birthday Example
Conditional Probability & Bayes' Theorem
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence Example 1
Independence Example 2
Discrete Random Variables
Random Variables
Probability Mass Function
PMF Example 1
PMF Example 2
Cumulative Distribution Function
CDF Example 1
Expected Value
Expected Value Example 1
Expected Value Example 2
Variance
Variance Example 1
Variance Example 2
Special Discrete Distributions
Discrete Uniform Distribution Part 1
Discrete Uniform Distribution Part 2
Example 1
Example 2
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 3
Example 4
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 5
Example 6
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 7
Example 8
Continuous Random Variables
Probability Density Function - PDF
Example 1
Cumulative Distribution Function - CDF
Example 2
Expected Value
Expected Value - Example 1
Expected Value - Example 2
Variance
Variance - Example 1
Special Continuous Distributions
Uniform Distribution
Example 1
Example 2
Exponential Distribution
Example 3
Example 4
Memoryless Property
Example 5
Normal Distribution
Standard Normal Distribution
Reading Z Table
Example 6
Discrete Joint Distributions
Probability Mass Function
PMF Example
Marginal PMF and CDF
Expected Value
Variance
Expected Value and Variance Example
Conditional PMF and CDF
Conditional Expectation
End of Topic Example - Part I
End of Topic Example - Part II
Continuous Joint Distributions
Introduction
Marginal PDF and CDF
Expected Value and Variance
Conditional PDF and CDF
Conditional Expectation
Example 1
Example 2
Example 3
Markov Chains
What is a Markov Chain?
One-Step Transition Probabilities
Example
n-Step Transition Probabilities
Chapman-Kolmogorov Equations
Example
Unconditional State Probabilities
Example
States and Steady-State Probabilities
Steady State Distribution
Example
Classes and State Properties
Periodicity and Ergodic Markov Chains
Example
Example
Example
Steady State Probability Applications
First Passage Times
Example
Expected First Passage Time
Expected Recurrence Time
Example
Sample Exam Problems
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Conditional Probability & Bayes' Theorem 1
Conditional Probability & Bayes' Theorem 2
Conditional Probability & Bayes' Theorem 3
Conditional Probability & Bayes' Theorem 4
Conditional Probability & Bayes' Theorem 5
Conditional Probability & Bayes' Theorem 6
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Discrete Random Variables 5
Special Discrete Distributions 1
Special Discrete Distributions 2
Special Discrete Distributions 3
Special Discrete Distributions 4
Special Discrete Distributions 5
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Special Continuous Distributions 1
Special Continuous Distributions 2
Special Continuous Distributions 3
Special Continuous Distributions 4
Special Continuous Distributions 5
Discrete Joint Distributions 1
Discrete Joint Distributions 2
Continuous Joint Distributions 1
Continuous Joint Distributions 2
Markov Chains 1
Markov Chains 2
Markov Chains 3
Markov Chains 4
States and Steady-State Probabilities 1
States and Steady-State Probabilities 2
States and Steady-State Probabilities 3
States and Steady-State Probabilities 4
States and Steady-State Probabilities 5
States and Steady-State Probabilities 6
States and Steady-State Probabilities 7
Steady State Probability Applications 1
Steady State Probability Applications 2
1599 TL
Ders Özeti
Sıkça Sorulan Sorular
Örneğin, Koç Üniversitesi - MATH 101 (Calculus) veya başka bir okulun benzer dersi olsun, paketlerimiz tam da o derse göre tasarlanır. Böylece nokta atışı çalışır, zaman kazanırsın.
Sınava özel videolar —konu anlatımları, çıkmış sorular ve çözümleri, özet notlar—içerir. Sınavda sıkça çıkan soruları hedefler. Eğitmenlerimiz, üniversitenin akademik takvimini takip ederek paketleri sürekli günceller. Böylece, gereksiz detaylarla vakit kaybetmeden başarını artırmaya odaklanabilirsin.