Erinevus lehekülje "Machine learning ITI8565" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
3. rida: 3. rida:
 
Previous years: [[Machine learning ITI8565 (2017)|2017]]
 
Previous years: [[Machine learning ITI8565 (2017)|2017]]
  
Spring 2016/2017
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Spring 2017/2018
  
 
ITI8565: Machine learning
 
ITI8565: Machine learning
13. rida: 13. rida:
 
Time and place:  
 
Time and place:  
 
   Lectures: Tuesdays 16:00-17:30  ICT-A1
 
   Lectures: Tuesdays 16:00-17:30  ICT-A1
   Labs:  Thursdays  16:00-17:30  ICT-402
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   Labs:  Thursdays  16:00-17:30  ICT-401
 
 
Consultation: There is no scheduled time for consultation this semester. If you need consultation just drom me an e-mail and I will find time to answer your questions!
 
  
 +
Consultation: TBA
 
   
 
   
 
 
 
Additional information: sven.nomm@ttu.ee
 
Additional information: sven.nomm@ttu.ee
  
32. rida: 29. rida:
  
 
=Lectures =
 
=Lectures =
Lecture slides, necessary files, links and other necessary information would be provided by means of Moodle (To be set up by 10.02.2017)
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== Lecture 1 Introduction and distance function ==
 
 
== Lecture 1: Intro ==
 
[[Media:Intro_and_DTrees_ML2017_1.pdf ‎|Slides]]
 
 
 
 
 
== Lecture 2: Distance and classification ==
 
[[Media:Lecture2_ML2017_kNN.pdf |Slides]]
 
 
 
 
 
== Lecture 3: Clustering I ==
 
[[Media:Lecture3_ML2017_Clustering.pdf |Slides]]
 
 
 
== Lecture 4: Clustering II ==
 
[[Media:Lecture4_ML2017_Clustering2.pdf |Slides]]
 
 
 
Moodle environment at ained.ttu.ee has been updated.
 

Redaktsioon: 29. jaanuar 2018, kell 14:00

Machine learning ITI8565

Previous years: 2017

Spring 2017/2018

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

Time and place:

 Lectures: Tuesdays 16:00-17:30  ICT-A1
 Labs:  Thursdays   16:00-17:30  ICT-401

Consultation: TBA

Additional information: sven.nomm@ttu.ee

Evaluation

  • 91 < score -- grade 5 (excellent)
  • 81 < score < 90 -- grade 4 (very good)
  • 71 < score < 80 -- grade 3 (good)
  • 61 < score < 70 -- grade 2 (satisfactory)
  • 51 < score < 60 -- grade 1 (acceptable)

score ≤ 50 -- a student has failed the course

Lectures

Lecture 1 Introduction and distance function