Course title: Machine Learning
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.
|Andrew Ng||MACHINE LEARNING YEARNING||2016|
|Christopher Bishop||Pattern Recognition And Machine Learning||Springer||2006|