Machine Learning

Објавено: October 12, 2018
  1.    Course Title Machine Learning
  2.    Code 3ФЕИТ01З008
  3.    Study program KHIE, KSIAR
  4.    Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
  5.    Degree (first, second, third cycle) First cycle
  6.    Academic year/semester IV/7   7.    Number of ECTS credits 6.00
  8.    Lecturer Dr Gorjan Nadjinski
  9.    Course Prerequisites

10.    Course Goals (acquired competencies):  Introduction to the basic machine learning principles, concepts, and techniques. Upon successful completion of the course, the students will be able to independently solve practical engineering tasks using machine learning algorithms.

11.    Course Syllabus: Introduction to machine learning. Supervised learning: Single-variable and multi-variable linear regression. Gradient descent method. Polynomial regression. Logistic regression. Classification as a machine learning problem. Regularization. Neural networks. Support vector machines. Unsupervised learning. k-means clustering. Feature compression. Anomaly detection systems. Machine learning for large data sets. Examples of machine learning algorithms implementation for real problem solving.
12.    Learning methods:  Combined: presentations, homework, project assignments, practical laboratory work.
13.    Total number of course hours 2 + 2 + 1 + 0
14.    Distribution of course hours 180
15.    Forms of teaching 15.1. Lectures-theoretical teaching 30
15.2. Exercises (laboratory, practice classes), seminars, teamwork 45
16.    Other course activities 16.1. Projects, seminar papers 45
16.2. Individual tasks 30
16.3. Homework and self-learning 30
17.    Grading 17.1. Exams 0
17.2. Seminar work/project (presentation: written and oral) 40
17.3. Activity and participation 0
17.4. Final exam 60
18.    Grading criteria (points) up to 50 points     5 (five) (F)
from 51 to 60 points     6 (six) (E)
from 61 to 70 points     7 (seven) (D)
from 71 to 80 points     8 (eight) (C)
from 81 to 90 points     9 (nine) (B)
from 91 to 100 points   10 (ten) (A)
19.    Conditions for acquiring teacher’s signature and for taking final exam Regular attendance at classes and completion of the laboratory work assignments.
20.  Forms of assessment Two partial written exams are envisaged during the semester (at the middle and the end of the semester, each with a duration of 120 minutes), as well as a mandatory project that the students are supposed to finish and present during the semester.
1. The students who have passed the partial exams and have successfully finished and presented the project, are considered to have passed the course. The presentation of the project is with duration not longer than 60 minutes. The final grade is formed based on the points from the partial exams and the points obtained from the project.
2. In the planned exam sessions a final written exam is taken (duration 120 minutes). The students who have passed the final written exam, and have finished and presented the mandatory project previously during the semester, are considered to have passed the course. The final grade is formed based on the points from the exams, and the points acquired from the project
21.   Language Macedonian and English
22.   Method of monitoring of teaching quality Internal evaluation and polls.
23.   Literature
23.1. Required Literature
No. Author Title Publisher Year
1 Andrew Ng MACHINE LEARNING YEARNING 2016
2 Christopher Bishop Pattern Recognition And Machine Learning Springer 2006