Bu ders ile ENGR 200 sınavı dersindeki temel olasılık ve istatistik konseptleri olan: Continuous Random Variables, Normal Distribution, Continuous Joint Probability, Covariance, Central Limit Theorem, Markov Chains kavramlarını çok iyi öğreneceksin ve her konu için bolca çıkmış sınav sorusuyla antreman yapacaksın.
Normal Distribution
Normal Distribution
Standard Normal Distribution
Reading Z Table - Option 1
Reading Z Table - Option 2
Example 3
Normal Approximation to Binomial Distribution
Example 4
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 Final Problems I
Normal Distribution 1
Normal Distribution 2
Normal Distribution 3
Normal Distribution 4
Normal Distribution 5
Normal Distribution 6
Normal Distribution 7
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Continuous Joint Probability 4
Continuous Joint Probability 5
Continuous Joint Probability 6
Continuous Joint Probability 7
Continuous Joint Probability 8
Continuous Joint Probability 9
Continuous Joint Probability 10
Continuous Joint Probability 11
Covariance and Correlation
Covariance
Covariance : Discrete Case
Covariance : Continuous Case 1
Covariance : Continuous Case 2
Exam Like Question 1
Variance of Sums
Example 1
Correlation
Example 2
Exam Like Question 2
Derived Distributions
Functions of Random Variables
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Sample Final Problems II
Covariance and Correlation 1
Covariance and Correlation 2
Covariance and Correlation 3
Covariance and Correlation 4
Covariance and Correlation 5
Covariance and Correlation 6
Covariance and Correlation 7
Covariance and Correlation 8
Covariance and Correlation 9
Derived Distributions 1
Derived Distributions 2
Derived Distributions 3
Derived Distributions 4
Derived Distributions 5
Derived Distributions 6
Derived Distributions 7
Derived Distributions 8
Derived Distributions 9
Central Limit Theorem
Introduction
Example 1
Example 2
Example 3
Example 4
Example 5
Random Processes: Markov Chains
Markov Property & One Step Transition Matrix
N-Step Transition Matrix
Unconditional Probabilities
Classification of States
Steady State Probabilities 1
Steady State Probabilities 2
Expected Recurrence Time
First Passage Time
Expected First Passage Time
Absorbing State
Sample Final Problems III
Central Limit Theorem 1
Central Limit Theorem 2
Central Limit Theorem 3
Central Limit Theorem 4
Central Limit Theorem 5
Central Limit Theorem 6
Random Processes: Markov Chains 1
Random Processes: Markov Chains 2
Random Processes: Markov Chains 3
Random Processes: Markov Chains 4
Sample Final
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Question 8
Question 9
Question 10
Question 11
Question 12
Question 13
Question 14
Question 15
Bonus : Sample Final (Midterm Subjects)
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Discrete Joint Distribution 1
Discrete Joint Distribution 2
Discrete Joint Distribution 3
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Exponential Distribution
Exponential Distribution 2
2011 yılında Endüstri Mühendisliği okumak için başladığım Sabancı Üniversitesi'nden 2018 yılında Bilgisayar Mühendisi olarak mezun oldum. 11 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.
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.
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