Fundamentals of Machine Learning
BASIC DATA
course listing
A - main register
course code
ICM0035
course title in Estonian
Masinõppe alused
course title in English
Fundamentals of Machine Learning
course volume CP
-
ECTS credits
6.00
to be declared
yes
fully online course
not
assessment form
Examination
teaching semester
autumn
language of instruction
Estonian
English
Study programmes that contain the course
code of the study programme version
course compulsory
IAAM17/25
yes
Structural units teaching the course
IC - IT College
Course description link
Timetable link
View the timetable
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Aine eesmärk on anda ülevaade masinõppest, seal kasutatavatest meetoditest ja printsiipidest, tulemuste hindamisest ja rakendatavusest, samuti arendada oskusi masinõppe mudelite koostamisel.
course aims in English
The goal is to give an overview of main principles and methods in machine learning. The applicability and assessment of machine learning models are handled as well. The skills of developing of machine learning models is an important goal as well.
learning outcomes in the course in Est.
Õppeaine läbinud üliõpilane:
- nimetab masinõppe põhimõisteid, -printsiipe, -meetodeid ja algoritme;
- hindab masinõppe mudelite kasutamise täpsust ja otstarbekust konkreetsete probleemide ning ülesannete lahendamisel;
- kasutab masinõppe-alast tarkvara konkreetsete probleemide lahendamisel.
learning outcomes in the course in Eng.
After completing this course, the student:
- formulates the key concepts, principles, algorithms and methods of machine learning;
- evaluates exactness and reasonability of applying of machine learning models in case of particular problems;
- utilizes machine learning oriented software for developing models.
brief description of the course in Estonian
Ülevaade masinõppe põhiprintsiipidest, eesmärkidest ja rakendusvaldkondadest. Käsitletakse juhendatud ja juhendamata õpet ning stiimulõpet. Meetoditest ja lähenemisviisidest vaadeldakse klasterdamist, mõõtmelisuse vähendamist, otsustuspuude põhiseid meetodeid, tehisnärvivõrke, Bayesi statistika põhiseid meetodeid, tugivektormasinaid, k lähima naabri põhist meetodit. Õpitakse ka mudeleid hindama. Praktiseeritakse ka eeltreenitud närvivõrkude adapteerimist konkreetsetele probleemidele.
brief description of the course in English
Overview of main principles, goals and scope of application of machine learning. Supervised, unsupervised and reinforcement learning are handled. Main methods like clustering, reduction of dimensionality, decision trees, artificial neural networks, Bayesian methods, support vector machines, kNN-methods are considered. The assessment of models is handled. Adapting of pretrained neural networks in case of particular problems is considered as well.
type of assessment in Estonian
-
type of assessment in English
-
independent study in Estonian
Mudelite koostamine ja häälestamine. Koduse uurimistöö koostamine.
independent study in English
Developing and tuning of models. Composing the research work.
study literature
- Russell, S.J and Norvig, P. Artificial intelligence: a modern approach, third edition, Prentice Hall. 2010. ISBN-13: 978-0-13-207148-2
- Christopher Bischop. Pattern Recognition and Machine Learning. ISBN-13: 978-0387310732. Springer 2011.
- Zielesny A. From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence. Springer 2018.
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
2.0
practices
-
exercises
0.0
exercises
-
lecturer in charge
Toomas Lepikult, dotsent (IC - IT kolledž)
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
Course-teacher pairs of the corresponding version are missing!
Course description in Estonian
Course description in English