Ma`lumotlar intelektual taxlili

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 1
1. What is feature selection(Atributlarni tanlash nima)?
2. What is not Data mining(Qaysi xolatlar ma'lumotlarni intellektual taxlili emas)?
Sodda savollar
1. filereader
2. data types
3. prediction
4. R
5. Java Ma`lumotlar intelektual taxlili API

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 2
1. Explain Appiriori algorithm(Appiriori algoritmini tushuntring)?
2. Describe backpropagation algorithm(Xatoliklarni qayta taqsimlash algoritmini tushuntring)?
Sodda savollar
1. classifier
2. Rapid Minder
3. data types
4. R
5. weka API

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 3
1. What is classification algorithms(sinflarga ajratish algoritmlari nima)?
2. what is lerarning artificial neural network(Suniy neyron to'rlarini o'qitish nima)?
Sodda savollar
1. k-means
2. testset
3. instances
4. prediction
5. Selection

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 4
1. Explain Appiriori algorithm(Appiriori algoritmini tushuntring)?
2. What is feature selection(Atributlarni tanlash nima)?
Sodda savollar
1. dataset
2. neural network
3. target data
4. Rapid Minder
5. Tanagra

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 5
1. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
2. what is desicion support systems(Qaror qabul qilish tizimlari nima)?
Sodda savollar
1. instances
2. features
3. decision tree
4. weka API
5. filereader

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 6
1. Explain Appiriori algorithm(Appiriori algoritmini tushuntring)?
2. what is neural network(Neyron to'rlari nima)?
Sodda savollar
1. prediction
2. weka API
3. neural network
4. xelops
5. weka

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 7
1. Explain Appiriori algorithm(Appiriori algoritmini tushuntring)?
2. what is lerarning artificial neural network(Suniy neyron to'rlarini o'qitish nima)?
Sodda savollar
1. objects
2. interactive table
3. c-means
4. filereader
5. association rule

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 8
1. What is OLTP(OLTP nima)?
2. What is feature selection(Atributlarni tanlash nima)?
Sodda savollar
1. target data
2. testset
3. decision tree
4. data types
5. appirori

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 9
1. What is Ma`lumotlar intelektual taxlili tools?
2. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
Sodda savollar
1. testset
2. Rapid Minder
3. instances
4. dataset
5. regression

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 10
1. what is lerarning artificial neural network(Suniy neyron to'rlarini o'qitish nima)?
2. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
Sodda savollar
1. weka
2. dataset
3. objects
4. c-means
5. testset

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 11
1. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
2. What is Classification(sinflarga ajratish nima)?
Sodda savollar
1. filereader
2. trainset
3. classifier
4. orange
5. target data

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 12
1. What is Ma`lumotlar intelektual taxlili(Ma'lumotlarni intellektual taxlili nima)?
2. What is Ma`lumotlar intelektual taxlili tools?
Sodda savollar
1. testset
2. orange
3. decision tree
4. interactive table
5. c-means

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 13
1. What is feature selection(Atributlarni tanlash nima)?
2. What is clustering(Sinflashtrish nima)?
Sodda savollar
1. Selection
2. c-means
3. prediction
4. neural network
5. trainset

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 14
1. What is feature selection(Atributlarni tanlash nima)?
2. What is Ma`lumotlar intelektual taxlili(Ma'lumotlarni intellektual taxlili nima)?
Sodda savollar
1. weka
2. neural network
3. weka API
4. preprossesing
5. c-means

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 15
1. What is feature selection(Atributlarni tanlash nima)?
2. Explain Appiriori algorithm(Appiriori algoritmini tushuntring)?
Sodda savollar
1. association rule
2. appirori
3. Rapid Minder
4. xelops
5. Selection

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 16
1. What is feature selection(Atributlarni tanlash nima)?
2. Describe backpropagation algorithm(Xatoliklarni qayta taqsimlash algoritmini tushuntring)?
Sodda savollar
1. association rule
2. neural network
3. testset
4. trainset
5. features

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 17
1. What is feature selection(Atributlarni tanlash nima)?
2. What is classification algorithms(sinflarga ajratish algoritmlari nima)?
Sodda savollar
1. c-means
2. decision tree
3. Rapid Minder
4. weka
5. k-means

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 18
1. What is Data mining tools?
2. What is OLTP(OLTP nima)?
Sodda savollar
1. prediction
2. xelops
3. instances
4. R
5. weka

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 19
1. What is Classification(sinflarga ajratish nima)?
2. What is classification algorithms(sinflarga ajratish algoritmlari nima)?
Sodda savollar
1. Rapid Minder
2. instances
3. c-means
4. R
5. Tanagra

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 20
1. What is pattern recognition(Timsollarni aniqlash nima)?
2. what is desicion support systems(Qaror qabul qilish tizimlari nima)?
Sodda savollar
1. dataset
2. Selection
3. preprossesing
4. Java Ma`lumotlar intelektual taxlili API
5. appirori

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 21
1. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
2. what is neural network(Neyron to'rlari nima)?
Sodda savollar
1. R
2. c-means
3. Rapid Minder
4. trainset
5. dataset

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 22
1. What is OLAP(OLAP nima)?
2. what is desicion support systems(Qaror qabul qilish tizimlari nima)?
Sodda savollar
1. classifier
2. regression
3. filereader
4. k-means
5. association rule

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 23
1. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
2. Explain Appiriori algorithm(Appiriori algoritmini tushuntring)?
Sodda savollar
1. features
2. regression
3. Rapid Minder
4. R
5. parametres

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 24
1. what is feedforward neural network(to'g'ri taqsimlangan neyron to'ri nima)?
2. What is pattern recognition(Timsollarni aniqlash nima)?
Sodda savollar
1. classifier
2. R
3. target data
4. appirori
5. data types

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 25
1. What is pattern recognition(Timsollarni aniqlash nima)?
2. Describe backpropagation algorithm(Xatoliklarni qayta taqsimlash algoritmini tushuntring)?
Sodda savollar
1. Rapid Minder
2. xelops
3. instances
4. features
5. neural network

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 26
1. what is desicion support systems(Qaror qabul qilish tizimlari nima)?
2. Ma`lumotlar intelektual taxlili tasks(MIT masalalari)
Sodda savollar
1. weka
2. preprossesing
3. testset
4. regression
5. instances

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 27
1. What is Ma`lumotlar intelektual taxlili tools?
2. what is association rule(Assosiative qoida nima)?
Sodda savollar
1. neural network
2. decision tree
3. orange
4. Rapid Minder
5. objects

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan

yakuniy nazorat
BILET № 28
1. what is association rule(Assosiative qoida nima)?
2. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
Sodda savollar
1. data types
2. c-means
3. dataset
4. objects
5. orange

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 29
1. Describe backpropagation algorithm(Xatoliklarni qayta taqsimlash algoritmini tushuntring)?
2. why we need feature selection(Atributlarni tanlash nima uchun kerak)?
Sodda savollar
1. Rapid Minder
2. weka API
3. features
4. Selection
5. interactive table

Kafedra mudri: Xo'jayev O.

________________________________

"Ma`lumotlar intelektual taxlili" fanidan
yakuniy nazorat
BILET № 30
1. what is association rule(Assosiative qoida nima)?
2. What is pattern recognition(Timsollarni aniqlash nima)?
Sodda savollar
1. decision tree
2. testset
3. Tanagra
4. features
5. weka API

Kafedra mudri: Xo'jayev O.