MATH 240 • Midterm I • Introduction to Probability & Statistics for Engineers
Olasılık ve İstatistik kadar hayatın içinden bir konsepti üniversitede bulmak çok zor.
Tadını çıkararak öğrendiğin ve okuluna özel soru tipleriyle sınava hazırlandığın bu dersi kaçırma!
Eğitmenler
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.
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.
Paketi Tamamla
🎓 TED Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
Konular
Counting, Combination and Permutation
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
Counting 1
Counting 2
Counting 3
Counting 4
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Conditional Probability 4
Independence 1
Independence 2
Conditional Probability and Independence
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Bayes' Rule 6
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
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 II
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Discrete Random Variables 5
Discrete Random Variables 6
Discrete Random Variable 7
Discrete Random Variables 8
Discrete Random Variable 9
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Random Variables 5
Continuous Random Variables 6
Binomial and Poisson 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
Continuous Uniform and Exponential Distributions
Continuous Uniform Distribution
Example 1
Example 2
Exponential Distribution
Example 3
Example 4
Memoryless Property
Example 5
Sample Midterm Problems III
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Binomial Distribution 5
Poisson Distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Poisson Distribution 5
Poisson Approximation to Binomial 1
Poisson Approximation to Binomial 2
Uniform Distribution 1
Uniform Distribution 2
Uniform Distribution 3
Exponential Distribution 1
Exponential Distribution 2
Exponential Distribution 3
Exponential Distribution 4
Exponential Distribution 5
Değerlendirmeler
Ders İçeriği
Counting, Combination and Permutation
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
Counting 1
Counting 2
Counting 3
Counting 4
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Conditional Probability 4
Independence 1
Independence 2
Conditional Probability and Independence
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Bayes' Rule 6
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
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 II
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Discrete Random Variables 5
Discrete Random Variables 6
Discrete Random Variable 7
Discrete Random Variables 8
Discrete Random Variable 9
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Random Variables 5
Continuous Random Variables 6
Binomial and Poisson 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
Continuous Uniform and Exponential Distributions
Continuous Uniform Distribution
Example 1
Example 2
Exponential Distribution
Example 3
Example 4
Memoryless Property
Example 5
Sample Midterm Problems III
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Binomial Distribution 5
Poisson Distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Poisson Distribution 5
Poisson Approximation to Binomial 1
Poisson Approximation to Binomial 2
Uniform Distribution 1
Uniform Distribution 2
Uniform Distribution 3
Exponential Distribution 1
Exponential Distribution 2
Exponential Distribution 3
Exponential Distribution 4
Exponential Distribution 5
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.