Erinevus lehekülje "Machine learning" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
 
(ei näidata 5 kasutaja 73 vahepealset redaktsiooni)
1. rida: 1. rida:
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Previous years: [https://courses.cs.ttu.ee/w/index.php?title=Machine_learning&oldid=440 2014]
  
Spring 2013/2014
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Spring 2014/2015
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
  
Taught by: Kairit Sirts
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Taught by: Sven Nõmm
  
 
EAP: 6.0
 
EAP: 6.0
  
Time and place: Fridays
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Time and place: Thursdays
   Lectures: 16:00-17:30  X-406
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   Lectures: 14:00-15:30  ICT-A2
   Labs: 17:45-19:15 X-412
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   Labs: 16:00-17:30 ICT-405
  
Additional information: sirts@ioc.ee, juhan.ernits@ttu.ee
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  Consultation: by appointment
  
Skype: kairit.sirts
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Additional information: sven.nomm@ttu.ee
  
 
The course is organised by [http://cs.ttu.ee the Department of Comptuer Science]. The course is supported by [http://studyitin.ee/ IT Academy].
 
The course is organised by [http://cs.ttu.ee the Department of Comptuer Science]. The course is supported by [http://studyitin.ee/ IT Academy].
 
Students should also subscribe to [http://lists.ttu.ee/mailman/listinfo/machine-learning machine learning list].
 
This is used to spread information about the course in this semester as well as any other machine learning related event happening in TUT (also in future).
 
 
'''New!!!''' Homework rankings based on results (just for fun): [[Meedia:Ranking.pdf|Ranking]] <br \>
 
This will be updated as the homework results are checked. Stay in tune!
 
 
 
  
 
== Lecture 1: Introduction, decision trees ==
 
== Lecture 1: Introduction, decision trees ==
[[Media:lecture1.pdf|Slides]]
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[[Media:Intro_and_DTrees_ML_1.pdf |Slides]]
  
 
[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?
 
[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?
33. rida: 27. rida:
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch01.pdf Reading] - contains also the full algorithm for decision tree learning with divide-and-conquer strategy.
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch01.pdf Reading] - contains also the full algorithm for decision tree learning with divide-and-conquer strategy.
  
[[Media:Hw1.pdf|First homework]] is open in moodle. For submitting you have to register [https://moodle.e-ope.ee/course/view.php?id=6504|target='_new' to the course]
 
  
== Lecture 2: K nearest neighbours ==
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== Lecture 2: k-nearest neighbors ==
[[Meedia:Lecture2.pdf|Slides]]
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[[Media:Intro_and_DTrees_ML2017_1.pdf |Slides]]
  
[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
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== Lecture 3: K-means & Gaussians  ==
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[[Media:Lecture3_ML2015_K_means.pdf ‎|Slides]]
  
== Lecture 3: K-means clustering, MLE principle ==
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NB!  Home assignment Nr.1 will be given next week
[[Meedia:Lecture3.pdf|Slides]]
 
  
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading I]
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading I]
47. rida: 40. rida:
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch13.pdf Reading II]
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch13.pdf Reading II]
  
== Lecture 4: Gaussian Mixture Model, EM algorithm ==
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== Lecture 4: Gaussian Mixture Model & EM algorithm ==
[[Meedia:Lecture4.pdf|Slides]]
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[[Media:Lecture4_ML2015_GMM_and_EM.pdf ‎|Slides]]
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch14.pdf Reading ]
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Home assignment Nr.1
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If you missed the class please contact the lecturer sven.nomm@gmail.com
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to receive your individual data and get assignment for the part 2.1.
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[[Media:HomeAssignmnet1.pdf | Home Assignmnet 1]]
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== Lecture 5: Linear Regression  ==
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[[Media:Lecture5_ML2015_Linear_Regression.pdf ‎|Slides]]
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[[Media: ML_Lecture5_data.zip|Data file 1 for the practice]]
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== Lecture 6: Logistic Regression  ==
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[[Media:Lecture6_ML2015_Logistic_Regression.pdf ‎|Slides]]
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== Home Assignment 1: Grades ==
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[[Media:Home Assignment 1 Grades.pdf ‎|Grades as for 16.03.2015]]
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== Lecture 7: Logistic Regression  ==
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[[Media:Lecture7_ML2015_Logistic_Regression_Model_Fitting.pdf ‎|Slides]]
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Home assignment Nr.2
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If you missed the class please contact the lecturer sven.nomm@gmail.com
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to receive your individual data.
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[[Media:Home Assignment 1 Grades_2303.pdf ‎|Grades as for 23.03.2015]]
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[[Media:HomeAssignmnet2.pdf | Home Assignmnet 2]]
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== Lecture 8: Artificial neural networks  ==
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[[Media:Lecture8_ML2015_Neural_Networks.pdf ‎|Slides]]
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[[Media: Lecture8_Practice.zip|Data file for the practice]]
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== Lecture 9: Competitive learning ==
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[[Media:Lecture9_ML2015_N_Competitive_Learning.pdf ‎|Slides]]
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[[Media: Lecture9_Practice.zip|Data file for the practice]]
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== Lecture 10: Neural networks ==
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[[Media:Neural Network Presentation for Machine Learning Class.pdf ‎|Slides]]
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== Lecture 11: Multiclass classification ==
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[[Media:Lecture11_ML2015_N_Multiclass_classification.pdf ‎|Slides]]
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== Home Assignment 3: Neural networks ==
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[[Media:HomeAssignmnet3.pdf ‎|Assignment]]
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[[Media:HomeAssignment3.zip ‎|Data]]
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== Lecture 12: Markov chains and hidden Markov models ==
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[[Media:Lecture12_ML2015_N_Markov_chains_and_hMm_1.pdf ‎|Slides]]
  
[http://ciml.info/dl/v0_8/ciml-v0_8-ch14.pdf Reading]
 
  
[[Media:Hw2.pdf|Second homework]] is open in moodle.
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== Lecture 13 ==
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NB! Thursday 30.04.2015 Lecture is cancelled!!! Instead of the lecture practice will take place at 14:00  ICT-405 !!!
  
== Lecture 5: History of neural networks, perceptron ==
 
[[Meedia:Lecture5.pdf|Slides]]
 
  
[http://ciml.info/dl/v0_8/ciml-v0_8-ch03.pdf Reading]
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== Final Project: description ==
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[[Media:description.pdf ‎|Final Poject: description]]
  
== Lecture 6: Artificial neural networks ==
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== Home Assignment 4 ==
[[Meedia:Lecture6.pdf|Slides]]
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[[Media:Home_assignment4.pdf ‎|Assignment]]
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[[Media:HomeAssignment_4.zip ‎|Data]]
  
[[Meedia:Bp_math.pdf|Backpropagation notes]]
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==Guest Lecture==
  
[[Media:Hw3.pdf|Third homework]] is open in moodle.
 
  
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[[Media:SVM_MK_2015.pdf ‎|Support vector Machines by Maria Kesa]]
  
== Additional links ==
 
[[Meedia:Latex_example.pdf|Latex example]]
 
  
[[Meedia:Latex_example.tex|Latex example code]]
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== Consultation ==
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21.05.2015  ICT-405  14:00- 17:30
  
[http://www.maths.tcd.ie/~dwilkins/LaTeXPrimer/ Latex tutorial]
 
  
[http://arkitus.com/patterns-for-research-in-machine-learning/ Tips for scientific programming]
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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!!!