Machine learning ITI8565 (2017)

Allikas: Kursused
Redaktsioon seisuga 30. juuni 2016, kell 11:23 kasutajalt Sven (arutelu | kaastöö) (→‎Lecture 9: Marcov Models)
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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: Gaussian Mixture Model and EM algorithm

Slides

Lecture 6: Neural Networks

Slides

Lecture 7: Logistic Regression

Slides

Lecture 8: Competitive learning

Slides

Lecture 9: Markov Models

Slides

Lecture 10: Multiclass classification

Slides

Lecture 11: Support vector machines

Slides

Lecture 12: Random forests

Slides