Machine Learning

Објавено: October 12, 2018

Course title: Machine Learning

Code:3ФЕИТ01З008

Number of credits (ECTS): 6

Weekly number of classes: 2+2+1+0

Prerequisite for enrollment of the subject: None

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.

Total available number of classes: 180

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 implementation learning algorithms for real problem solving.

Literature:

Required Literature

No.

Author

Title

Publisher

Year

1

Andrew NgMACHINE LEARNING YEARNING 2016

2

Christopher BishopPattern Recognition And Machine LearningSpringer2006