Ders Bilgileri

#### Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
PROBABILITY AND COMPUTATIONAL STATISTICS CMM 522 0 3 + 0 3 6
 Dersin Dili Türkçe Dersin Seviyesi Yüksek Lisans Dersin Türü SECMELI Dersin Koordinatörü Dr.Öğr.Üyesi UFUK KULA Dersi Verenler Dersin Yardımcıları Dersin Kategorisi Dersin Amacı This objective of the course is to understand the basic concepts of probability and statistics and to apply these concepts into data obtained in the relevant research areas in order to analyze, extract useful information from these data and make statistical inferences. Dersin İçeriği Random Experiment, Events, Sample Space; Probability Axioms, Some important rules of Probability; Conditional Probability, Total Probability Formula, Bayes? Theorem; Random variables and some important Probability Distributions; Sampling and some Sampling Distributions; Explanatory Data Analysis, Pattern Recognition Techniques; Supervised and Unsupervised Learning; Regression Analysis
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri 1 - Define a Random Experiment, Event, Sample Space and List these concept in a given Random Experiment. 1 - 2 - 3 - 5 - A - B - C - 2 - Prove basic probability theorems by using the probability axioms. 1 - 2 - 3 - 5 - A - C - 3 - Compute the event probabilities by using the concepts and rules of conditional probability. 1 - 2 - 3 - 5 - A - C - 4 - Use the concept of sampling distribution in a given problem, compute and interpret the probabilities of the related statistics. 1 - 2 - 3 - 5 - A - B - C - 5 - Find and interpret the existing patterns in a given data set by using pattern recognition techniques. 1 - 2 - 3 - 5 - A - B - C - 6 - Make statistical inferences by applying Monte Carlo techniques. 1 - 2 - 3 - 5 - A - B - C - 7 - Distinguish between Supervised and Unsupervised Learning, Apply these techniques to a given data set. 1 - 2 - 3 - 5 - A - B - C -
 Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 3:Discussion 5:Demonstration Ölçme Yöntemleri: A:Testing B:Oral Exam C:Homework

#### Ders Akışı

Hafta Konular ÖnHazırlık
1 Random Experiment, Events, Sample Space and Probability Measure, Probability Axioms
2 Important Rules of of Probability and Conditional Probability
3 Total Probability Formula and Bayes? Formula
4 Random Variables, and Probability Distributions
5 Expectation and Variance of a Random Variable
6 Sampling and Sampling Distributions
7 Exploratory Data Analysis
8 Exploratory Data Analysis
9 Finding Structures in Data: Principal Component Analysis, Independent Component Analysis
10 Statistical Hypothesis Testing
11 Statistical Hypothesis Testing and Monte Carlo Methods in Inferential Statistics
12 Supervised Learning
13 Unsuprevised Learning
14 Regression Analysis

#### Kaynaklar

Ders Notu Lecture notes can be found under "MATERIAL SHARING" section at the start of the semester.
Ders Kaynakları 1. Computational Statistics Handbook with MATLAB, (2008), Martinez W. L. Martinez A.R., Springer
2. Elements of Computational Statistics, 2005, Gentle J., Chapman and Hall

#### Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5

#### Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 60
KisaSinav 1 10
Odev 1 5
Odev 2 5
Odev 3 5
Odev 4 5
Odev 5 5
Odev 6 5
Toplam 100
Yıliçinin Başarıya Oranı 60
Finalin Başarıya Oranı 40
Toplam 100

#### AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 12 12
Quiz 1 3 3
Assignment 6 3 18
Final examination 1 12 12
Toplam İş Yükü 141
Toplam İş Yükü /25(s) 5.64
Dersin AKTS Kredisi 5.64
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