MATH 250 • Midterm • Introduction to Probability
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Eğitmenler
İhsan Altundağ
Eğitmen
2007 yılında Galatasaray Üniversitesi Bilgisayar Mühendisliği bölümünden birincilikle mezun olduktan sonra Fransa'da Kriptoloji üzerine Fransa hükümeti tarafından verilen bursla yüksek lisans yaptım. Devamında ikinci kez sınava girerek Boğaziçi Matematik bölümünü de bitirdim. Yaklaşık 15 yıldır üniversite öğrencilerine dersler vermekteyim.
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.
Sınava 5 gün kaldı.
Paketi Tamamla
🎓 Bilkent Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.

MATH 250 • Final
Introduction to Probability
İhsan Altundağ
1599 TL

MATH 250 • Midterm
Introduction to Probability
İhsan Altundağ
1699 TL
Konular
Counting Rules
Basic Principles of Counting
Counting Examples
Permutations
Permutations Example
Groups and Circular Permutation Example
Identical Objects Example 1
Identical Objects Example 2
Combination
n choose r
Committee Example 1
Ball Example 1
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, Independence, Bayes' Rule
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence Example 1
Independence Example 2
Sample Midterm Problems I
Counting 1
Axioms of Probability 1
Axioms of Probability 2
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Conditional Probability 4
Conditional Probability 5
Conditional Probability 6
Conditional Probability 7
Conditional Probability 8
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
Bernoulli, Binomial and Negative Binomial Distributions
Bernoulli Distribution - 1
Bernoulli Distribution - 2
Example 1
Example 2
Binomial Distribution - 1
Binomial Distribution - 2
Example 3
Example 4
Negative Binomial Distribution - 1
Negative Binomial Distribution - 2
Example 5
Example 6
Geometric, Hypergeometric and Poisson Distributions
Geometric Distribution - 1
Geometric Distribution - 2
Example 1
Example 2
Hypergeometric Distribution
Example 3
Poisson Distribution - 1
Poisson Distribution - 2
Example 4
Example 5
Poisson Approximation to Binomial Distribution
Example 6
Sample Midterm Problems II
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Binomial Distribution 5
Binomial Distribution 6
Binomial Distribution 7
Binomial Distribution 8
Binomial Distribution 9
Binomial Distribution 10
Negative Binomial Distribution 1
Negative Binomial Distribution 2
Negative Binomial Distribution 3
Negative Binomial Distribution 4
Negative Binomial Distribution 5
Negative Binomial Distribution 6
Geometric Distribution 1
Geometric Distribution 2
Geometric Distribution 3
Geometric Distribution 4
Geometric Distribution 5
Geometric Distribution 6
Geometric Distribution 7
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
Sample Midterm Problems III
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Question 8
Question 9
Special Continuous Probability 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 - Option 1
Reading Z Table - Option 2
Example 6
Gamma Distribution
Example 7
Relation of Gamma Distribution with Others
Exam Practice: Fall 2024 Exam
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Exam Practice: Spring 2025 Exam
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Değerlendirmeler
Ders İçeriği
Counting Rules
Basic Principles of Counting
Counting Examples
Permutations
Permutations Example
Groups and Circular Permutation Example
Identical Objects Example 1
Identical Objects Example 2
Combination
n choose r
Committee Example 1
Ball Example 1
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, Independence, Bayes' Rule
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence Example 1
Independence Example 2
Sample Midterm Problems I
Counting 1
Axioms of Probability 1
Axioms of Probability 2
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Conditional Probability 4
Conditional Probability 5
Conditional Probability 6
Conditional Probability 7
Conditional Probability 8
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
Bernoulli, Binomial and Negative Binomial Distributions
Bernoulli Distribution - 1
Bernoulli Distribution - 2
Example 1
Example 2
Binomial Distribution - 1
Binomial Distribution - 2
Example 3
Example 4
Negative Binomial Distribution - 1
Negative Binomial Distribution - 2
Example 5
Example 6
Geometric, Hypergeometric and Poisson Distributions
Geometric Distribution - 1
Geometric Distribution - 2
Example 1
Example 2
Hypergeometric Distribution
Example 3
Poisson Distribution - 1
Poisson Distribution - 2
Example 4
Example 5
Poisson Approximation to Binomial Distribution
Example 6
Sample Midterm Problems II
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Binomial Distribution 5
Binomial Distribution 6
Binomial Distribution 7
Binomial Distribution 8
Binomial Distribution 9
Binomial Distribution 10
Negative Binomial Distribution 1
Negative Binomial Distribution 2
Negative Binomial Distribution 3
Negative Binomial Distribution 4
Negative Binomial Distribution 5
Negative Binomial Distribution 6
Geometric Distribution 1
Geometric Distribution 2
Geometric Distribution 3
Geometric Distribution 4
Geometric Distribution 5
Geometric Distribution 6
Geometric Distribution 7
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
Sample Midterm Problems III
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Question 8
Question 9
Special Continuous Probability 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 - Option 1
Reading Z Table - Option 2
Example 6
Gamma Distribution
Example 7
Relation of Gamma Distribution with Others
Exam Practice: Fall 2024 Exam
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Exam Practice: Spring 2025 Exam
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Sınava 5 gün kaldı.
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.