CENG 235 • Midterm • Introduction to Probability and Statistics
Olasılıkla başlayıp, İstatistikle biten bu dersimizde özet ve uygulamaları konu anlatımlarıyla temelleri atıyor; sayısız çözümlü soru örneğiyle sınavlara hazır hale geliyoruz!
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.
Geçme Garantisi
Derslerimize çok güveniyoruz. Dersi geçememen çok zor ama yine de geçemezsen paran iade.
Tüm koşullarPaketi Tamamla
🎓 Çankaya Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
Konular
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' Rule and Independence
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
Sample Midterm Problems I
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Conditional Probability and Independence 1
Conditional Probability and Independence 2
Conditional Probability and Independence 3
Conditional Probability and Independence 4
Conditional Probability and Independence 5
Conditional Probability and Independence 6
Conditional Probability and Independence 7
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Bayes' Rule 6
Bayes' Rule 7
Discrete Random Variables and Mathematical Expectation
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
Continuous Random Variables and Mathematical Expectation
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 II
Discrete Random Variable 1
Discrete Random Variable 2
Discrete Random Variable 3
Discrete Random Variable 4
Discrete Random Variable 5
Discrete Random Variable 6 - Expected Value
Discrete Random Variable 7 - Expected Value
Discrete Random Variable 8 - Variance
Discrete Random Variable 9 - Expected Value
Continuous Random Variable 1
Continuous Random Variable 2
Continuous Random Variable 3
Continuous Random Variable 4
Continuous Random Variable 5 - Expected Value
Continuous Random Variable 6 - Expected Value
Continuous Random Variable 7 - Variance
Continous Random Variable 8 - Expected Value
Discrete Joint Probability
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 Probability
Introduction
Marginal PDF and CDF
Expected Value and Variance
Conditional PDF and CDF
Conditional Expectation
Example 1
Example 2
Example 3
Sample Midterm Problems III
Discrete Joint Probability 1
Discrete Joint Probability 2
Discrete Joint Probability 3 - Expected Value
Discrete Joint Probability 4 - Expected Value
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Continuous Joint Probability 4
Continuous Joint Probability 5 - Expected Value
Special Discrete Probability Distributions
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 1
Example 2
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 3
Example 4
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 5
Example 6
Poisson Approximation to Binomial Distribution
Example 7
Hypergeometric Distribution
Example 8
Normal Approximation to Binomial Distribution
Example 9
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 10
Example 11
Negative Binomial Distribution Part 1
Negative Binomial Distribution Part 2
Example 12
Example 13
Sample Midterm Problems IV
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Multinomial Distribution 1
Multinomial Distribution 2
Poisson distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Hypergeometric Distribution 1
Hypergeometric Distribution 2
Hypergeometric Distribution 3
Hypergeometric Distribution 4
Hypergeometric Distribution 5
Negative Binomial Distribution 1
Negative Binomial - Geometric Distribution
Değerlendirmeler
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' Rule and Independence
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
Sample Midterm Problems I
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Conditional Probability and Independence 1
Conditional Probability and Independence 2
Conditional Probability and Independence 3
Conditional Probability and Independence 4
Conditional Probability and Independence 5
Conditional Probability and Independence 6
Conditional Probability and Independence 7
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Bayes' Rule 6
Bayes' Rule 7
Discrete Random Variables and Mathematical Expectation
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
Continuous Random Variables and Mathematical Expectation
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 II
Discrete Random Variable 1
Discrete Random Variable 2
Discrete Random Variable 3
Discrete Random Variable 4
Discrete Random Variable 5
Discrete Random Variable 6 - Expected Value
Discrete Random Variable 7 - Expected Value
Discrete Random Variable 8 - Variance
Discrete Random Variable 9 - Expected Value
Continuous Random Variable 1
Continuous Random Variable 2
Continuous Random Variable 3
Continuous Random Variable 4
Continuous Random Variable 5 - Expected Value
Continuous Random Variable 6 - Expected Value
Continuous Random Variable 7 - Variance
Continous Random Variable 8 - Expected Value
Discrete Joint Probability
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 Probability
Introduction
Marginal PDF and CDF
Expected Value and Variance
Conditional PDF and CDF
Conditional Expectation
Example 1
Example 2
Example 3
Sample Midterm Problems III
Discrete Joint Probability 1
Discrete Joint Probability 2
Discrete Joint Probability 3 - Expected Value
Discrete Joint Probability 4 - Expected Value
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Continuous Joint Probability 4
Continuous Joint Probability 5 - Expected Value
Special Discrete Probability Distributions
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 1
Example 2
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 3
Example 4
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 5
Example 6
Poisson Approximation to Binomial Distribution
Example 7
Hypergeometric Distribution
Example 8
Normal Approximation to Binomial Distribution
Example 9
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 10
Example 11
Negative Binomial Distribution Part 1
Negative Binomial Distribution Part 2
Example 12
Example 13
Sample Midterm Problems IV
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Multinomial Distribution 1
Multinomial Distribution 2
Poisson distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Hypergeometric Distribution 1
Hypergeometric Distribution 2
Hypergeometric Distribution 3
Hypergeometric Distribution 4
Hypergeometric Distribution 5
Negative Binomial Distribution 1
Negative Binomial - Geometric Distribution
Geçme Garantisi
Derslerimize çok güveniyoruz. Dersi geçememen çok zor ama yine de geçemezsen paran iade.
Tüm koşullarSıkça 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.