Erinevus lehekülje "Machine learning" redaktsioonide vahel
Mine navigeerimisribale
Mine otsikasti
| (ei näidata 5 kasutaja 73 vahepealset redaktsiooni) | |||
| 1. rida: | 1. rida: | ||
| + | Previous years: [https://courses.cs.ttu.ee/w/index.php?title=Machine_learning&oldid=440 2014] | ||
| − | Spring | + | Spring 2014/2015 |
ITI8565: Machine learning | ITI8565: Machine learning | ||
| − | Taught by: | + | Taught by: Sven Nõmm |
EAP: 6.0 | EAP: 6.0 | ||
| − | Time and place: | + | Time and place: Thursdays |
| − | Lectures: | + | Lectures: 14:00-15:30 ICT-A2 |
| − | Labs: | + | Labs: 16:00-17:30 ICT-405 |
| − | + | Consultation: by appointment | |
| − | + | ||
| + | Additional information: sven.nomm@ttu.ee | ||
The course is organised by [http://cs.ttu.ee the Department of Comptuer Science]. The course is supported by [http://studyitin.ee/ IT Academy]. | The course is organised by [http://cs.ttu.ee the Department of Comptuer Science]. The course is supported by [http://studyitin.ee/ IT Academy]. | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
== Lecture 1: Introduction, decision trees == | == Lecture 1: Introduction, decision trees == | ||
| − | [[Media: | + | [[Media:Intro_and_DTrees_ML_1.pdf |Slides]] |
[[Media:Dt_example.pdf|Example made in class]] - When to play tennis? | [[Media:Dt_example.pdf|Example made in class]] - When to play tennis? | ||
| 33. rida: | 27. 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. | [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: | + | == Lecture 2: k-nearest neighbors == |
| − | [[ | + | [[Media:Intro_and_DTrees_ML2017_1.pdf |Slides]] |
| − | [ | + | == Lecture 3: K-means & Gaussians == |
| + | [[Media:Lecture3_ML2015_K_means.pdf |Slides]] | ||
| − | + | NB! Home assignment Nr.1 will be given next week | |
| − | |||
[http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading I] | [http://ciml.info/dl/v0_8/ciml-v0_8-ch02.pdf Reading I] | ||
| 47. rida: | 40. rida: | ||
[http://ciml.info/dl/v0_8/ciml-v0_8-ch13.pdf Reading II] | [http://ciml.info/dl/v0_8/ciml-v0_8-ch13.pdf Reading II] | ||
| − | == Lecture 4: Gaussian Mixture Model | + | == Lecture 4: Gaussian Mixture Model & EM algorithm == |
| − | [[ | + | [[Media:Lecture4_ML2015_GMM_and_EM.pdf |Slides]] |
| + | |||
| + | [http://ciml.info/dl/v0_8/ciml-v0_8-ch14.pdf Reading ] | ||
| + | |||
| + | 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. | ||
| + | |||
| + | [[Media:HomeAssignmnet1.pdf | Home Assignmnet 1]] | ||
| + | |||
| + | == Lecture 5: Linear Regression == | ||
| + | [[Media:Lecture5_ML2015_Linear_Regression.pdf |Slides]] | ||
| + | |||
| + | [[Media: ML_Lecture5_data.zip|Data file 1 for the practice]] | ||
| + | |||
| + | == Lecture 6: Logistic Regression == | ||
| + | [[Media:Lecture6_ML2015_Logistic_Regression.pdf |Slides]] | ||
| + | |||
| + | == Home Assignment 1: Grades == | ||
| + | [[Media:Home Assignment 1 Grades.pdf |Grades as for 16.03.2015]] | ||
| + | |||
| + | == Lecture 7: Logistic Regression == | ||
| + | [[Media:Lecture7_ML2015_Logistic_Regression_Model_Fitting.pdf |Slides]] | ||
| + | |||
| + | Home assignment Nr.2 | ||
| + | If you missed the class please contact the lecturer sven.nomm@gmail.com | ||
| + | to receive your individual data. | ||
| + | |||
| + | [[Media:Home Assignment 1 Grades_2303.pdf |Grades as for 23.03.2015]] | ||
| + | |||
| + | [[Media:HomeAssignmnet2.pdf | Home Assignmnet 2]] | ||
| + | |||
| + | == Lecture 8: Artificial neural networks == | ||
| + | [[Media:Lecture8_ML2015_Neural_Networks.pdf |Slides]] | ||
| + | |||
| + | [[Media: Lecture8_Practice.zip|Data file for the practice]] | ||
| + | |||
| + | |||
| + | == Lecture 9: Competitive learning == | ||
| + | [[Media:Lecture9_ML2015_N_Competitive_Learning.pdf |Slides]] | ||
| + | |||
| + | [[Media: Lecture9_Practice.zip|Data file for the practice]] | ||
| + | |||
| + | |||
| + | == Lecture 10: Neural networks == | ||
| + | [[Media:Neural Network Presentation for Machine Learning Class.pdf |Slides]] | ||
| + | |||
| + | |||
| + | == Lecture 11: Multiclass classification == | ||
| + | [[Media:Lecture11_ML2015_N_Multiclass_classification.pdf |Slides]] | ||
| + | |||
| + | == Home Assignment 3: Neural networks == | ||
| + | [[Media:HomeAssignmnet3.pdf |Assignment]] | ||
| + | [[Media:HomeAssignment3.zip |Data]] | ||
| + | |||
| + | |||
| + | == Lecture 12: Markov chains and hidden Markov models == | ||
| + | [[Media:Lecture12_ML2015_N_Markov_chains_and_hMm_1.pdf |Slides]] | ||
| − | |||
| − | + | == 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 == |
| + | [[Media:description.pdf |Final Poject: description]] | ||
| − | == | + | == Home Assignment 4 == |
| − | [[ | + | [[Media:Home_assignment4.pdf |Assignment]] |
| + | [[Media:HomeAssignment_4.zip |Data]] | ||
| − | + | ==Guest Lecture== | |
| − | |||
| + | [[Media:SVM_MK_2015.pdf |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!!! | ||
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!!!