IE 211 • Midterm • Probability Theory
Her mühendisliğin temelinde var olan Olasılık ve İstatistik konularının uygulamalarını gördüğümüz bu derste: 1) Probability 2) Conditional Probability and Bayes' Rule 3) Discrete Random Variables 4) Continuous Random Variables konularını inceliyoruz.
Sınava yönelik sorular ile konuların en kritik soru tiplerini kolaydan zora çözüp sınavda sürprize yer bırakmıyoruz!
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.
Memnuniyet Garantisi
Bu dersi alma kararını senin için kolaylaştıralım. Eğer memnun kalmazsan 30 gün içinde bize ulaş, 3'ten fazla içerik tamamlamadıysan iade alabilirsin.
Tüm koşullarPaketi Tamamla
🎓 Türk Hava Kurumu Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
IE 211 • Midterm
Probability Theory
İhsan Altundağ
1299 TL
IE 211 • Final
Probability Theory
İhsan Altundağ
1199 TL
Konular
Introduction to Statistics and Data Analysis
Introduction
Frequency Distribution
Example 1
Relative Frequency Distribution
Example 2
Cumulative Frequency Distribution
Example 3
Frequency Histogram
Measures of Central Location
Example 4
Measures of Variability
Example 5
Example 6
Example 7
Skewness and Kurtosis
Stem and Leaf Diagram
Example 8
Example 9
Box and Whisker Plot
Example 10
Comparing Boxplots
Example 11
Example 12
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
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
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
Random Variables and Probability Distributions
Random Variables
Probability Mass Function (PMF) for Discrete RV
PMF Example 1
PMF Example 2
Cumulative Distribution Function for Discrete RV
CDF Example
Probability Density Function (PDF) for Continuous RV
PDF Example
Cumulative Distribution Function (CDF) for Continuous RV
CDF Example
Discrete Joint Random Variables
Discrete Joint RV Example
Marginal PMF and CDF for Discrete Joint RV
Conditional PMF and CDF for Discrete Joint RV
Continuous Joint Random Variables
Marginal PDF and CDF for Continuous Joint RV
Conditional PDF and CDF for Continuous Joint RV
Mathematical Expectation
Expected Value for Discrete RV
Example 1
Example 2
Variance for Discrete RV
Example 3
Example 4
Expected Value for Continuous RV
Example 5
Example 6
Variance for Continuous RV
Example 7
Expected Value for Discrete Joint RV
Variance for Discrete Joint RV
Example 8
Conditional Expectation for Discrete Joint RV
Example 9
Example 10
Expected Value and Variance for Continuous Joint RV
Conditional Expectation for Continuous Joint RV
Example 11
Example 12
Example 13
Covariance
Correlation
Chebyshev's Theorem
Example 14
Sample Midterm Problems
Descriptive Statistics 1
Descriptive Statistics 2
Descriptive Statistics 3
Descriptive Statistics 4
Descriptive Statistics 5
Descriptive Statistics 6
Counting 1
Counting 2
Counting 3
Counting 4
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Axioms of Probability 5
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
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Discrete Random Variable 1
Discrete Random Variable 2
Discrete Random Variable 3
Discrete Random Variable 4
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Discrete Joint Probability 1
Discrete Joint Probability 2
Discrete Joint Probability 3
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Mathematical Expectation 1 - Discrete R.V.
Mathematical Expectation 2 - Discrete R.V.
Mathematical Expectation 3 - Discrete R.V
Mathematical Expectation 4 - Discrete R.V.
Mathematical Expectation 5 - Discrete R.V.
Mathematical Expectation 6 - Continuous R.V.
Mathematical Expectation 7 - Continuous R.V.
Mathematical Expectation 8 - Discrete Joint
Mathematical Expectation 9 - Continuous Joint
Mathematical Expectation 10 - Continuous Joint
Mathematical Expectation 11 - Covariance
Mathematical Expectation 12 - Covariance
Mathematical Expectation 13 - Covariance
Chebyshev's Theorem 1
Chebyshev's Theorem 2
Değerlendirmeler
Henüz hiç değerlendirme yok.
Ders İçeriği
Introduction to Statistics and Data Analysis
Introduction
Frequency Distribution
Example 1
Relative Frequency Distribution
Example 2
Cumulative Frequency Distribution
Example 3
Frequency Histogram
Measures of Central Location
Example 4
Measures of Variability
Example 5
Example 6
Example 7
Skewness and Kurtosis
Stem and Leaf Diagram
Example 8
Example 9
Box and Whisker Plot
Example 10
Comparing Boxplots
Example 11
Example 12
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
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
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
Random Variables and Probability Distributions
Random Variables
Probability Mass Function (PMF) for Discrete RV
PMF Example 1
PMF Example 2
Cumulative Distribution Function for Discrete RV
CDF Example
Probability Density Function (PDF) for Continuous RV
PDF Example
Cumulative Distribution Function (CDF) for Continuous RV
CDF Example
Discrete Joint Random Variables
Discrete Joint RV Example
Marginal PMF and CDF for Discrete Joint RV
Conditional PMF and CDF for Discrete Joint RV
Continuous Joint Random Variables
Marginal PDF and CDF for Continuous Joint RV
Conditional PDF and CDF for Continuous Joint RV
Mathematical Expectation
Expected Value for Discrete RV
Example 1
Example 2
Variance for Discrete RV
Example 3
Example 4
Expected Value for Continuous RV
Example 5
Example 6
Variance for Continuous RV
Example 7
Expected Value for Discrete Joint RV
Variance for Discrete Joint RV
Example 8
Conditional Expectation for Discrete Joint RV
Example 9
Example 10
Expected Value and Variance for Continuous Joint RV
Conditional Expectation for Continuous Joint RV
Example 11
Example 12
Example 13
Covariance
Correlation
Chebyshev's Theorem
Example 14
Sample Midterm Problems
Descriptive Statistics 1
Descriptive Statistics 2
Descriptive Statistics 3
Descriptive Statistics 4
Descriptive Statistics 5
Descriptive Statistics 6
Counting 1
Counting 2
Counting 3
Counting 4
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Axioms of Probability 5
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
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Discrete Random Variable 1
Discrete Random Variable 2
Discrete Random Variable 3
Discrete Random Variable 4
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Discrete Joint Probability 1
Discrete Joint Probability 2
Discrete Joint Probability 3
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Mathematical Expectation 1 - Discrete R.V.
Mathematical Expectation 2 - Discrete R.V.
Mathematical Expectation 3 - Discrete R.V
Mathematical Expectation 4 - Discrete R.V.
Mathematical Expectation 5 - Discrete R.V.
Mathematical Expectation 6 - Continuous R.V.
Mathematical Expectation 7 - Continuous R.V.
Mathematical Expectation 8 - Discrete Joint
Mathematical Expectation 9 - Continuous Joint
Mathematical Expectation 10 - Continuous Joint
Mathematical Expectation 11 - Covariance
Mathematical Expectation 12 - Covariance
Mathematical Expectation 13 - Covariance
Chebyshev's Theorem 1
Chebyshev's Theorem 2
Memnuniyet Garantisi
Bu dersi alma kararını senin için kolaylaştıralım. Eğer memnun kalmazsan 30 gün içinde bize ulaş, 3'ten fazla içerik tamamlamadıysan iade alabilirsin.
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.