Erinevus lehekülje "Machine learning" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
(Process mining slides added.)
 
(ei näidata 5 kasutaja 46 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]
  
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Spring 2013/2014
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Spring 2014/2015
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
  
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Taught by: Kairit Sirts
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Taught by: Sven Nõmm
  
 
EAP: 6.0
 
EAP: 6.0
  
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Time and place: Fridays
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Time and place: Thursdays
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   Lectures: 16:00-17:30  X-406
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   Lectures: 14:00-15:30  ICT-A2
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   Labs: 17:45-19:15 X-412
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   Labs: 16:00-17:30 ICT-405
  
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   Consultation:
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   Consultation: by appointment
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  30.05.2014 at 15:00 in ICT-411
 
  
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  Exams:
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  06.06.2014 at 16:00 in ICT-411
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Additional information: sven.nomm@ttu.ee
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  13.06.2014 at 16:00 in ICT-411
 
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  Additional exam:
 
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  19.06.2014 at 18:00 in ICT-411
 
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Additional information: sirts@ioc.ee, juhan.ernits@ttu.ee
 
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Skype: kairit.sirts
 
  
 
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].
  
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Students should also subscribe to [http://lists.ttu.ee/mailman/listinfo/machine-learning machine learning list].
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== Lecture 1: Introduction, decision trees ==
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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).
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[[Media:Intro_and_DTrees_ML_1.pdf ‎|Slides]]
  
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Homework rankings based on results (just for fun): [[Meedia:Ranking.pdf|Ranking]] <br \>
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[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?
  
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No lecture on 18.04.2014. Instead of that, we will have a joint session for solving homework problems on Thursday 17.04 starting from 14:00 in ICT-411.
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[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.
  
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== Assignments ==
 
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[[Media:Hw1.pdf|First homework]] about decision trees 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]
 
  
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[[Media:Hw2.pdf|Second homework]] about KNN and K-means is open in moodle.
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== Lecture 2: k-nearest neighbors ==
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[[Media:Intro_and_DTrees_ML2017_1.pdf ‎|Slides]]
  
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[[Media:Hw3.pdf|Third homework]] about neural networks is open in moodle.
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== Lecture 3: K-means & Gaussians  ==
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[[Media:Lecture3_ML2015_K_means.pdf ‎|Slides]]
  
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[https://www.dropbox.com/sh/50sioj7j8z7rwfn/s_iLJ6VlA0 Data] for the third homework
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NB!  Home assignment Nr.1 will be given next week
  
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[[Media:Hw4.pdf|Fourth homework]] about linear and logistic regression is open in moodle.
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading I]
  
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[[Media:Sbp.txt|Data]] for the fourth homework
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch13.pdf Reading II]
  
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[[Media:Hw5.pdf|Fifth homework]] about naive Bayes is open in moodle.
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== Lecture 4: Gaussian Mixture Model & EM algorithm  ==
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[[Media:Lecture4_ML2015_GMM_and_EM.pdf ‎|Slides]]
  
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[[Media:Spambase.txt|Data]] for the fifth homework
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch14.pdf Reading ]
  
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[[Media:Hw6.pdf|Sixth homework]] about support vector machines is open in moodle.
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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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== Lecture 1: Introduction, decision trees ==
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[[Media:HomeAssignmnet1.pdf | Home Assignmnet 1]]
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[[Media:lecture1.pdf|Slides]]
 
  
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[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?
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== Lecture 5: Linear Regression  ==
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[[Media:Lecture5_ML2015_Linear_Regression.pdf ‎|Slides]]
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[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.
 
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== Lecture 2: K nearest neighbours ==
 
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[[Meedia:Lecture2.pdf|Slides]]
 
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
 
  
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== Lecture 3: K-means clustering, MLE principle ==
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[[Media: ML_Lecture5_data.zip|Data file 1 for the practice]]
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[[Meedia:Lecture3.pdf|Slides]]
 
  
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading I]
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== Lecture 6: Logistic Regression  ==
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[[Media:Lecture6_ML2015_Logistic_Regression.pdf ‎|Slides]]
  
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch13.pdf Reading II]
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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 4: Gaussian Mixture Model, EM algorithm ==
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== Lecture 7: Logistic Regression  ==
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[[Meedia:Lecture4.pdf|Slides]]
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[[Media:Lecture7_ML2015_Logistic_Regression_Model_Fitting.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.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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== Lecture 5: History of neural networks, perceptron ==
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[[Media:HomeAssignmnet2.pdf | Home Assignmnet 2]]
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[[Meedia:Lecture5.pdf|Slides]]
 
  
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch03.pdf Reading]
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== Lecture 8: Artificial neural networks  ==
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[[Media:Lecture8_ML2015_Neural_Networks.pdf ‎|Slides]]
  
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== Lecture 6: Artificial neural networks ==
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[[Media: Lecture8_Practice.zip|Data file for the practice]]
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[[Meedia:Lecture6.pdf|Slides]]
 
  
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[[Meedia:Bp_math.pdf|Backpropagation notes]]
 
  
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch08.pdf Reading]
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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 7: Linear regresssion ==
 
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[[Meedia:Lecture7.pdf|Slides]]
 
  
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== Lecture 8: Logistic regresssion ==
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== Lecture 10: Neural networks ==
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[[Meedia:Lecture8.pdf|Slides]]
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[[Media:Neural Network Presentation for Machine Learning Class.pdf ‎|Slides]]
  
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== Lecture 9: Naive Bayes, maximum entropy model ==
 
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[[Meedia:Lecture9.pdf|Slides]]
 
  
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[http://see.stanford.edu/materials/aimlcs229/cs229-notes2.pdf Reading about Naive Bayes, section 2, lecture notes by Andrew Ng]
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== Lecture 11: Multiclass classification ==
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[[Media:Lecture11_ML2015_N_Multiclass_classification.pdf ‎|Slides]]
  
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[http://www.cs.jhu.edu/~jason/tutorials/loglin/#1 Tutorial about log-linear modeling by Jason Eisner]
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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 10: Sequence modeling ==
 
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[[Meedia:Lecture10.pdf|Slides]]
 
  
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[http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf Reading] The classic paper on HMM-s
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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]]
  
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== Lecture 11: Dimensionality reduction - PCA ==
 
  
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== Lecture 12: Support vector machines ==
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== Lecture 13 ==
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[[Meedia:Lecture12.pdf|Slides]]
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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 !!!
  
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[http://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf Reading, sections 1-4, lecture notes by Andrew Ng]
 
  
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== Lecture 13: SVM and kernels ==
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== Final Project: description ==
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[[Meedia:Lecture13.pdf|Slides]]
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[[Media:description.pdf ‎|Final Poject: description]]
  
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[http://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf Reading, sections 5-8, lecture notes by Andrew Ng]
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== Home Assignment 4 ==
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[[Media:Home_assignment4.pdf ‎|Assignment]]
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[[Media:HomeAssignment_4.zip ‎|Data]]
  
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== Lecture 14: Kernelized methods, Gaussian processes ==
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==Guest Lecture==
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[[Meedia:Lecture14.pdf|Slides]]
 
  
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== Lecture 15: Process mining. The alpha algorithm ==
 
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[[Meedia:Masinõpe.pdf|Slides]]
 
  
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[http://www.processmining.org/_media/processminingbook/process_mining_chapter_05_process_discovery.pdf| The alpha algorithm sliedes from Processmining.org]
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[[Media:SVM_MK_2015.pdf ‎|Support vector Machines by Maria Kesa]]
  
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== Additional links ==
 
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[[Meedia:Latex_example.pdf|Latex example]]
 
  
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[[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
  
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[http://www.maths.tcd.ie/~dwilkins/LaTeXPrimer/ Latex tutorial]
 
  
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[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!!!

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