Erinevus lehekülje "Machine learning" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
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[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
 
[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading]
 
[[Meedia:Latex_example.pdf|Latex example]]
 
 
[[Meedia:Latex_example.tex|Latex example code]]
 
 
[http://www.maths.tcd.ie/~dwilkins/LaTeXPrimer/ Latex tutorial]
 
  
 
== Lecture 3: K-means clustering, MLE principle ==
 
== Lecture 3: K-means clustering, MLE principle ==
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== Additional links ==
 
== 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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[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]
 
[http://arkitus.com/patterns-for-research-in-machine-learning/ Tips for scientific programming]

Redaktsioon: 13. märts 2014, kell 17:51

Spring 2013/2014

ITI8565: Machine learning

Taught by: Kairit Sirts

EAP: 6.0

Time and place: Fridays

 Lectures: 16:00-17:30  X-406
 Labs: 17:45-19:15  X-412

Additional information: sirts@ioc.ee, juhan.ernits@ttu.ee

Skype: kairit.sirts

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

Students should also subscribe to 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): Ranking
This will be updated as the homework results are checked. Stay in tune!


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.

First homework is open in moodle. For submitting you have to register to the course

Lecture 2: K nearest neighbours

Slides

Reading

Lecture 3: K-means clustering, MLE principle

Slides

Reading I

Reading II

Lecture 4: Gaussian Mixture Model, EM algorithm

Slides

Reading

Second homework is open in moodle.

Lecture 5: History of neural networks, perceptron

Slides

Reading


Additional links

Latex example

Latex example code

Latex tutorial

Tips for scientific programming