Erinevus lehekülje "Machine learning" redaktsioonide vahel
Mine navigeerimisribale
Mine otsikasti
| 45. rida: | 45. rida: | ||
== Lecture 4: Gaussian Mixture Model & EM algorithm == | == Lecture 4: Gaussian Mixture Model & EM algorithm == | ||
[[Media:Lecture4_ML2015_GMM_and_EM.pdf |Slides]] | [[Media:Lecture4_ML2015_GMM_and_EM.pdf |Slides]] | ||
| + | |||
| + | [http://ciml.info/dl/v0_8/ciml-v0_8-ch14.pdf Reading ] | ||
Home assignment Nr.1 | Home assignment Nr.1 | ||
| 51. rida: | 53. rida: | ||
[[Media:HomeAssignmnet1.pdf | Home Assignmnet 1]] | [[Media:HomeAssignmnet1.pdf | Home Assignmnet 1]] | ||
| + | |||
| + | == Lecture 5: Linear Regression == | ||
| + | [[Media:Lecture5_ML2015_Linear_Regression.pdf |Slides]] | ||
| + | |||
| + | [[Media: apt_data.mat|Data file 1 for the practice]] | ||
| + | [[Media: courier_data.mat|Data file 2 for the practice]] | ||
Redaktsioon: 5. märts 2015, kell 10:50
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
Data file for the practice Reading
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.