Kairit: Kairit laadis üles faili "Pilt:Ranking.pdf" uue versiooni

Machine learning

2014-06-05T13:00:15Z

Kairit: /* Lecture 11: Dimensionality reduction - PCA */

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

Consultation:
30.05.2014 at 15:00 in ICT-411

Exams:
06.06.2014 at 16:00 in ICT-411
13.06.2014 at 16:00 in ICT-411

Additional exam:
19.06.2014 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]] 

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 ==
[[Media:lecture1.pdf|Slides]]

[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?

[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]]

Machine learning

2014-06-05T12:59:53Z

Kairit:

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

Consultation:
30.05.2014 at 15:00 in ICT-411

Exams:
06.06.2014 at 16:00 in ICT-411
13.06.2014 at 16:00 in ICT-411

Additional exam:
19.06.2014 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]] 

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 ==
[[Media:lecture1.pdf|Slides]]

[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?

[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]
[http://www.ee.columbia.edu/~dpwe/e6820/papers/HyvO00-icatut.pdf Tutorial on ICA]

== 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]]

Fail:Ranking.pdf

2014-06-02T21:02:06Z

Kairit: Kairit laadis üles faili "Pilt:Ranking.pdf" uue versiooni

Fail:SampleExam2.pdf

2014-06-02T18:08:17Z

Kairit:

Machine learning

2014-06-02T18:07:57Z

Kairit:

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

Consultation:
30.05.2014 at 15:00 in ICT-411

Exams:
06.06.2014 at 16:00 in ICT-411
13.06.2014 at 16:00 in ICT-411

Additional exam:
19.06.2014 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]] 

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 ==
[[Media:lecture1.pdf|Slides]]

[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?

[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 ==

== 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]]

Machine learning

2014-06-02T18:06:29Z

2014-05-26T14:18:49Z

Kairit: Kairit laadis üles faili "Pilt:Ranking.pdf" uue versiooni

2014-05-10T15:07:05Z

Kairit:

Machine learning

2014-04-09T14:43:26Z

Kairit:

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 [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]] 
This will be updated as the homework results are checked. Stay in tune!

== Lecture 1: Introduction, decision trees ==
[[Media:lecture1.pdf|Slides]]

[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?

[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 ==
[[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]

[[Media:Hw2.pdf|Second homework]] is open in moodle.

== 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]

[[Media:Hw3.pdf|Third homework]] is open in moodle.

[https://www.dropbox.com/sh/50sioj7j8z7rwfn/s_iLJ6VlA0 Data] for the third homework

== Lecture 7: Linear regresssion ==
[[Meedia:Lecture7.pdf|Slides]]

== Lecture 8: Logistic regresssion ==
[[Meedia:Lecture8.pdf|Slides]]

== Lecture 8: 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]

== 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]

Machine learning

2014-04-09T14:35:45Z

Kairit:

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 [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]] 
This will be updated as the homework results are checked. Stay in tune!

== Lecture 1: Introduction, decision trees ==
[[Media:lecture1.pdf|Slides]]

[[Media:Dt_example.pdf|Example made in class]] - When to play tennis?

[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 ==
[[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]

[[Media:Hw2.pdf|Second homework]] is open in moodle.

== 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]

[[Media:Hw3.pdf|Third homework]] is open in moodle.

[https://www.dropbox.com/sh/50sioj7j8z7rwfn/s_iLJ6VlA0 Data] for the third homework

== Lecture 7: Linear regresssion ==
[[Meedia:Lecture7.pdf|Slides]]

== Lecture 8: Logistic regresssion ==
[[Meedia:Lecture8.pdf|Slides]]

== Lecture 8: 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]

== 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]