Goal:Introduction to theoretical concepts and practical skills in the field of machine learning and data processing.
Outcome:After completion of the course, students will be trained to use libraries for data processing within the Python programming language, form feature vector, apply feature reduction methods and algorithms for classification, clustering and regression.
Contents of the course
Theoretical instruction:
Introduction to machine learning.
Feature engineering.
Inductive empirical learning of functional estimation.
Supervised, unsupervised and reinforcement learning.
Bayesian decision rules.
Classification.
Textual document classification.
Clustering.
Regression and prediction.
Artificial neural networks and deep learning.
Support Vector Machines.
Feature vector dimensionality reduction.
Advanced text classification (sentiment analysis).
Follows theoretical lessons and enables students to solve practical problems in the field of machine learning using Python programming language and associated libraries.
Textbooks and References
D. Julian (2016): "Designing Machine Learning systems with Python". Packt Publishing.
L. P. Coelho, W. Richert (2015): "Building Machine Learning systems with Python, Second Edition". Packt Publishing.
M. Milosavljević (2015): "Veštačka inteligencija". Univerzitet Singidunium, Beograd.
J. Bell (2015): "Machine Learning: Hands-On for Developers and Technical Professionals". John Wiley & Sons, Inc.
