Erinevus lehekülje "Machine learning ITI8565 (2017)" redaktsioonide vahel

Allikas: Kursused
Mine navigeerimisribale Mine otsikasti
87. rida: 87. rida:
  
 
=Lecture 9:  =
 
=Lecture 9:  =
Due to a number of requests to postpone the Test Nr1.
 
Test Nr1. will take place on Thursday April the 7th.
 
 
On march the 31st studies will take place as usually.
 
 
[[Media:Lecture9_ML2016_N_Markov_chains_and_hMm.pdf |Slides]]
 
[[Media:Lecture9_ML2016_N_Markov_chains_and_hMm.pdf |Slides]]
  
=Test 1: 07.04.2016 =
+
=Lecture 10:   =
Nothing is scheduled for today  practice, but the room will be opened for your self practice. 
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[[Media:Lecture11_ML2015_N_Multiclass_classification.pdf |Slides]]
Sven will be around to answer your questions.
 
There will be no consultation today
 
 
 
=28.04 =
 
Test 1 Make-Up will take place on 28.04.2016 during the lecture.  
 
  
Practice will take place at its usual time.
+
Lecture 11:
 +
[[Media:Lecture11_ML2016_SVM.pdf |Slides]]
  
=19.05=
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Lecture 12:
Reminder: No lecture today.  
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[[Media:Lecture12_ML2016_RandomForests.pdf |Slides]]
We will meet in the computer class 16:00
 

Redaktsioon: 30. juuni 2016, kell 11:18

Previous years: 2015

Spring 2015/2015

ITI8565: Machine learning

Taught by: Sven Nõmm

EAP: 6.0

Time and place: Thursdays

 Lectures: 14:00-15:30  ICT-A1
 Labs: 16:00-17:30  ICT-402

Preliminary Information:

Examinations and consultations ICT-405:

26.05 Consultation 14:00-15:30

02.06 Exam 1 14:00-15:30

10.06 Exam 2 16:00-17:30

14.06 Make-up Exam 14:00-15:30


 Consultation: by appointment TBA


Additional information: sven.nomm@ttu.ee

The course is organised by the Department of Comptuer Science. The course is supported by IT Academy.

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 to pass

Lectures

Lecture slides, necessary files, links and other necessary information would appear here before the lecture or practice.

Lecture 1: Introduction and Decision Trees

Slides

Practice 1

Code and data examples

Please Observe the practice room change starting 12.02.2016 ICT-402 !!!

Lecture 2: k- Nearest Neighbors

Slides

Reading

Practice 2

Data


Lecture 3: K- Means

Slides

NB! Moodle environment for the course has been activated

If you need the code to enroll please contact the teacher by e-mail. I will continue to upload lecture slides here. All other resources including home assignments will be available thorough the moodle only!!!

Lecture 4: Linear Regression

Slides

Home Assignment 1

Home Assignment 1 is available in Moodle! The deadline is 15.03.2016 23:55 !

Lecture 5:

Slides

Lecture 6:

Slides

Lecture 7:

Slides

Lecture 8:

Slides

Lecture 9:

Slides

Lecture 10:

Slides

Lecture 11: Slides

Lecture 12: Slides