Erinevus lehekülje "Machine learning" redaktsioonide vahel
| (ei näidata sama kasutaja 11 vahepealset redaktsiooni) | |||
| 29. rida: | 29. rida: | ||
== Lecture 2: k-nearest neighbors == | == Lecture 2: k-nearest neighbors == | ||
| − | [[Media: | + | [[Media:Intro_and_DTrees_ML2017_1.pdf |Slides]] |
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== Lecture 3: K-means & Gaussians == | == Lecture 3: K-means & Gaussians == | ||
| 97. rida: | 94. rida: | ||
== Home Assignment 3: Neural networks == | == Home Assignment 3: Neural networks == | ||
[[Media:HomeAssignmnet3.pdf |Assignment]] | [[Media:HomeAssignmnet3.pdf |Assignment]] | ||
| + | [[Media:HomeAssignment3.zip |Data]] | ||
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| + | == Lecture 12: Markov chains and hidden Markov models == | ||
| + | [[Media:Lecture12_ML2015_N_Markov_chains_and_hMm_1.pdf |Slides]] | ||
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| + | == Lecture 13 == | ||
| + | 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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| + | == Final Project: description == | ||
| + | [[Media:description.pdf |Final Poject: description]] | ||
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| + | == Home Assignment 4 == | ||
| + | [[Media:Home_assignment4.pdf |Assignment]] | ||
| + | [[Media:HomeAssignment_4.zip |Data]] | ||
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| + | ==Guest Lecture== | ||
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| + | [[Media:SVM_MK_2015.pdf |Support vector Machines by Maria Kesa]] | ||
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| + | == Consultation == | ||
| + | 21.05.2015 ICT-405 14:00- 17:30 | ||
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| + | ==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!!! | ||
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
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
Lecture 3: K-means & Gaussians
NB! Home assignment Nr.1 will be given next week
Lecture 4: Gaussian Mixture Model & EM algorithm
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.
Lecture 5: Linear Regression
Lecture 6: Logistic Regression
Home Assignment 1: Grades
Lecture 7: Logistic Regression
Home assignment Nr.2 If you missed the class please contact the lecturer sven.nomm@gmail.com to receive your individual data.
Lecture 8: Artificial neural networks
Lecture 9: Competitive learning
Lecture 10: Neural networks
Lecture 11: Multiclass classification
Home Assignment 3: Neural networks
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
Home Assignment 4
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!!!