Erinevus lehekülje "Machine learning" redaktsioonide vahel

Redaktsioon: 6. veebruar 2015, kell 11:56

Previous years: 2014

Spring 2013/2014

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

Time and place: Thursdays

 Lectures: 14:00-15:30  ICT-A2
 Labs: 17:45-19:15  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.

@@ 5. rida: / 5. rida: @@
 ITI8565: Machine learning
-Taught by: Kairit Sirts
+Taught by: Sven Nõmm
 EAP: 6.0
-Time and place: Fridays
+Time and place: Thursdays
-   Lectures: 16:00-17:30  X-406
+   Lectures: 14:00-15:30  ICT-A2
-   Labs: 17:45-19:15  X-412
+   Labs: 17:45-19:15  ICT-405
    Consultation:
-.05.2014 at 15:00 in ICT-411
+   by appointment
-  Exams:
-.06.2014 at 16:00 in ICT-411
-.06.2014 at 16:00 in ICT-411
-  Additional exam:
+Additional information: sven.nomm@ttu.ee
-.06.2014 at 18:00 in ICT-411
-Additional information: sirts@ioc.ee, juhan.ernits@ttu.ee
-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].
-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).
-Homework rankings based on results (just for fun): [[Meedia:Ranking.pdf|Ranking]] <br \>
-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.
-== Assignments ==
-[[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]
-[[Media:Hw2.pdf|Second homework]] about KNN and K-means is open in moodle.
-[[Media:Hw3.pdf|Third homework]] about neural networks is open in moodle.
-[https://www.dropbox.com/sh/50sioj7j8z7rwfn/s_iLJ6VlA0 Data] for the third homework
-[[Media:Hw4.pdf|Fourth homework]] about linear and logistic regression is open in moodle.
-[[Media:Sbp.txt|Data]] for the fourth homework
-[[Media:Hw5.pdf|Fifth homework]] about naive Bayes is open in moodle.
-[[Media:Spambase.txt|Data]] for the fifth homework
-[[Media:Hw6.pdf|Sixth homework]] about support vector machines is open in moodle.
 == Lecture 1: Introduction, decision trees ==
@@ 61. rida: / 28. 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.
-== Lecture 2: K nearest neighbours ==
-[[Meedia:Lecture2.pdf|Slides]]
-[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
-== Lecture 3: K-means clustering, MLE principle ==
-[[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-ch13.pdf Reading II]
-== Lecture 4: Gaussian Mixture Model, EM algorithm ==
-[[Meedia:Lecture4.pdf|Slides]]
-[http://ciml.info/dl/v0_8/ciml-v0_8-ch14.pdf Reading]
-== Lecture 5: History of neural networks, perceptron ==
-[[Meedia:Lecture5.pdf|Slides]]
-[http://ciml.info/dl/v0_8/ciml-v0_8-ch03.pdf Reading]
-== Lecture 6: Artificial neural networks ==
-[[Meedia:Lecture6.pdf|Slides]]
-[[Meedia:Bp_math.pdf|Backpropagation notes]]
-[http://ciml.info/dl/v0_8/ciml-v0_8-ch08.pdf Reading]
-== Lecture 7: Linear regresssion ==
-[[Meedia:Lecture7.pdf|Slides]]
-== Lecture 8: Logistic regresssion ==
-[[Meedia:Lecture8.pdf|Slides]]
-== Lecture 9: Naive Bayes, maximum entropy model ==
-[[Meedia:Lecture9.pdf|Slides]]
-[http://see.stanford.edu/materials/aimlcs229/cs229-notes2.pdf Reading about Naive Bayes, section 2, lecture notes by Andrew Ng]
-[http://www.cs.jhu.edu/~jason/tutorials/loglin/#1 Tutorial about log-linear modeling by Jason Eisner]
-== Lecture 10: Sequence modeling ==
-[[Meedia:Lecture10.pdf|Slides]]
-[http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf Reading] The classic paper on HMM-s
-== Lecture 11: Dimensionality reduction - PCA ==
-[http://www.cs.princeton.edu/picasso/mats/PCA-Tutorial-Intuition_jp.pdf Tutorial on PCA]
-== Lecture 12: Support vector machines ==
-[[Meedia:Lecture12.pdf|Slides]]
-[http://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf Reading, sections 1-4, lecture notes by Andrew Ng]
-== Lecture 13: SVM and kernels ==
-[[Meedia:Lecture13.pdf|Slides]]
-[http://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf Reading, sections 5-8, lecture notes by Andrew Ng]
-== Lecture 14: Kernelized methods, Gaussian processes ==
-[[Meedia:Lecture14.pdf|Slides]]
-== Lecture 15: Process mining. The alpha algorithm ==
-[http://courses.cs.ttu.ee/w/images/e/e3/Masinõpe15.pdf Slides]
-[http://www.processmining.org/_media/processminingbook/process_mining_chapter_05_process_discovery.pdf The alpha algorithm slides from Processmining.org]
-== Additional links ==
-[[Meedia:Latex_example.pdf|Latex example]]
-[[Meedia:Latex_example.tex|Latex example code]]
-[http://www.maths.tcd.ie/~dwilkins/LaTeXPrimer/ Latex tutorial]
-[http://arkitus.com/patterns-for-research-in-machine-learning/ Tips for scientific programming]
-== Exam ==
-[[Meedia:SampleExam2.pdf|Example exam questions]]

Erinevus lehekülje "Machine learning" redaktsioonide vahel

Redaktsioon: 6. veebruar 2015, kell 11:56

Lecture 1: Introduction, decision trees

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