Erinevus lehekülje "Machine learning" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
 
(ei näidata sama kasutaja 5 vahepealset redaktsiooni)
29. rida: 29. rida:
  
 
== Lecture 2: k-nearest neighbors ==
 
== Lecture 2: k-nearest neighbors ==
[[Media:Lecture2_ML2015_KNN.pdf ‎|Slides]]
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[[Media:Intro_and_DTrees_ML2017_1.pdf ‎|Slides]]
 
 
[[Media: data_lecture2.zip|Data file for the practice]]
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
 
  
 
== Lecture 3: K-means & Gaussians  ==
 
== Lecture 3: K-means & Gaussians  ==
112. rida: 109. rida:
  
 
== Home Assignment 4 ==
 
== Home Assignment 4 ==
[[Media:Home_assignment_4.pdf ‎|Assignment]]
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[[Media:Home_assignment4.pdf ‎|Assignment]]
 
[[Media:HomeAssignment_4.zip ‎|Data]]
 
[[Media:HomeAssignment_4.zip ‎|Data]]
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==Guest Lecture==
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[[Media:SVM_MK_2015.pdf ‎|Support vector Machines by Maria Kesa]]
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== Consultation ==
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21.05.2015  ICT-405  14:00- 17:30
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==Exam 28.05.2015 ==
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Due to the ICT-405 availability examination time is shifted from 16:00 to 12:00
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If you could not come at 12 please let me know!!!

Viimane redaktsioon: 31. jaanuar 2017, kell 15:47

Previous years: 2014

Spring 2014/2015

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

Time and place: Thursdays

 Lectures: 14:00-15:30  ICT-A2
 Labs: 16:00-17:30  ICT-405
 Consultation: by appointment


Additional information: sven.nomm@ttu.ee

The course is organised by the Department of Comptuer Science. The course is supported by IT Academy.

Lecture 1: Introduction, decision trees

Slides

Example made in class - When to play tennis?

Reading - contains also the full algorithm for decision tree learning with divide-and-conquer strategy.


Lecture 2: k-nearest neighbors

Slides

Lecture 3: K-means & Gaussians

Slides

NB! Home assignment Nr.1 will be given next week

Reading I

Reading II

Lecture 4: Gaussian Mixture Model & EM algorithm

Slides

Reading

Home assignment Nr.1 If you missed the class please contact the lecturer sven.nomm@gmail.com to receive your individual data and get assignment for the part 2.1.

Home Assignmnet 1

Lecture 5: Linear Regression

Slides

Data file 1 for the practice

Lecture 6: Logistic Regression

Slides

Home Assignment 1: Grades

Grades as for 16.03.2015

Lecture 7: Logistic Regression

Slides

Home assignment Nr.2 If you missed the class please contact the lecturer sven.nomm@gmail.com to receive your individual data.

Grades as for 23.03.2015

Home Assignmnet 2

Lecture 8: Artificial neural networks

Slides

Data file for the practice


Lecture 9: Competitive learning

Slides

Data file for the practice


Lecture 10: Neural networks

Slides


Lecture 11: Multiclass classification

Slides

Home Assignment 3: Neural networks

Assignment Data


Lecture 12: Markov chains and hidden Markov models

Slides


Lecture 13

NB! Thursday 30.04.2015 Lecture is cancelled!!! Instead of the lecture practice will take place at 14:00 ICT-405 !!!


Final Project: description

Final Poject: description

Home Assignment 4

Assignment Data

Guest Lecture

Support vector Machines by Maria Kesa


Consultation

21.05.2015 ICT-405 14:00- 17:30


Exam 28.05.2015

Due to the ICT-405 availability examination time is shifted from 16:00 to 12:00 If you could not come at 12 please let me know!!!