| 1. | Course Title | Mathematical Methods for Machine Learning | |||||||||||
| 2. | Code | 4ФЕИТ08009 | |||||||||||
| 3. | Study program | 21-PNEIT | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Vesna Andova | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
The ability to define, understand and solve creative professional challenges of applied mathematics. The ability of professional communication in the native language as well as in English. The first part of the course deals with chapters from Linear algebra, and the second part covers topics from Statistics that are the basis of Machine Learning. |
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| 11. | Course Syllabus:
Scalars, vectors, and matrices. Geometry of matrix multiplication. Affine spaces. Geometry of vector spaces. Diagonazible matrices. SVD. Basis of graphs and adjacency matrix. Left and right eigenvectors of graph matrices. Application for vertex classification. Distributions derived from the normal distribution. Types of data. Random sample, sampling, generating a random sample. Data reduction. Parametric and non-parametric testing. Analysis of categorical data. |
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| 12. | Learning methods:
Lectures, exercises, individual work, and homework assignments. |
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| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | 30 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 30 points | |||||||||||
| 17.3. | Activity and participation | 0 points | |||||||||||
| 17.4. | Final exam | 40 points | |||||||||||
| 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 of classes/consultations | |||||||||||
| 20. | Forms of assessment | The assessment will be done continuously by midterm exams, homework/project and final exam. If a student does not take the midterm exams, he/she has to take written problem exam. | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | G.Casella, R.L. Berger |
Statistical Inference |
Dxbury Thompson Learning | 2002 | |||||||||
| 2. | P. Lancaster, M. Tismenetsky | The Theory of Matrices | Academic Press | 2007 | |||||||||
| 3. | G. Strang | Linear Algebra and Learning from Data | Wellesley-Cambridge Press, | 2019 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | G. Strang | Introduction to Linear algebra | Wellesley-Cambridge Press | 2016 | |||||||||
