Код: 365482

Назва:

Машинне навчання

Анотація: The course “Machine Learning” is aimed at familiarizing students with the main tasks, concepts, and models of machine learning. It includes consideration of supervised learning tasks, unsupervised learning tasks, and best practices of machine learning. The course provides the completion of practical assignments aimed at consolidating theoretical knowledge and applying the considered methods to applied problems. A prerequisite for studying the course is knowledge of the basic concepts of linear algebra, probability theory and statistics, as well as programming. The course is mandatory for students of the second (master’s) level of higher education in the first year of study within the specialty “Applied Mathematics.” Forms of assessment include laboratory assignments and continuous assessment. The final semester assessment is conducted in the form of an exam.


Рекомендована література: Primary:
1. Christopher M. Bishop. Pattern Recognition and Machine Learning
2. Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep learning
3. Trevor Hastie. Robert Tibshirani. Jerome Friedman. Elements of statistical learning
Secondary:
1. Aurйlien Gйron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd ed.,
O’Reilly, 2022.
2. Kevin P. Murphy. Probabilistic Machine Learning: An Introduction. MIT Press, 2022.
3. Franзois Chollet. Deep Learning with Python. 2nd ed., Manning, 2021.
4. Shai Shalev-Shwartz, Shai Ben-David. Understanding Machine Learning: From Theory to
Algorithms. Cambridge University Press, 2014.
5. 1. Shvai, N., Hasnat, A., Meicler, A. and Nakib, A., 2019. Accurate Classification for Automatic
Vehicle-Type Recognition Based on Ensemble Classifiers. IEEE Transactions on Intelligent
Transportation Systems.