Machine learning

Allikas: Kursused
Redaktsioon seisuga 2. juuni 2014, kell 18:07 kasutajalt Kairit (arutelu | kaastöö)
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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 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).

Homework rankings based on results (just for fun): 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

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

Second homework about KNN and K-means is open in moodle.

Third homework about neural networks is open in moodle.

Data for the third homework

Fourth homework about linear and logistic regression is open in moodle.

Data for the fourth homework

Fifth homework about naive Bayes is open in moodle.

Data for the fifth homework

Sixth homework about support vector machines is open in moodle.

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 neighbours

Slides

Reading

Lecture 3: K-means clustering, MLE principle

Slides

Reading I

Reading II

Lecture 4: Gaussian Mixture Model, EM algorithm

Slides

Reading


Lecture 5: History of neural networks, perceptron

Slides

Reading

Lecture 6: Artificial neural networks

Slides

Backpropagation notes

Reading


Lecture 7: Linear regresssion

Slides

Lecture 8: Logistic regresssion

Slides

Lecture 9: Naive Bayes, maximum entropy model

Slides

Reading about Naive Bayes, section 2, lecture notes by Andrew Ng

Tutorial about log-linear modeling by Jason Eisner

Lecture 10: Sequence modeling

Slides

Reading The classic paper on HMM-s

Lecture 11: Dimensionality reduction - PCA

Lecture 12: Support vector machines

Slides

Reading, sections 1-4, lecture notes by Andrew Ng

Lecture 13: SVM and kernels

Slides

Reading, sections 5-8, lecture notes by Andrew Ng

Lecture 14: Kernelized methods, Gaussian processes

Slides

Lecture 15: Process mining. The alpha algorithm

Slides

The alpha algorithm slides from Processmining.org

Additional links

Latex example

Latex example code

Latex tutorial

Tips for scientific programming

Exam

Example exam questions