Infopaket

Machine Learning in Earth Sciences

ОК 4

Обов’язкова дисципліна для ОП

Second

2025/2026

2 Semester

4

Know: the main methods of machine learning; the basic principles of organizing classification features; the basic principles of organizing artificial neural networks; the main Python modules used for machine learning. Be able to: implement algorithms using machine learning methods to solve given problems; mathematically prove the optimality of the machine learning method used to solve given problems; independently select machine learning tools in Python (Scikit-learn, Keras, PyTorch, NumPy, Pandas); use the Python IDE and PyCharm programming environments.

External form

Students should know: the basics of information technology, programming, and mathematical statistics. Students should be able to: use the Python programming language to solve basic problems.

This course covers both the theoretical and practical foundations of machine learning using Python. Students study and explore the key concepts and algorithms that underlie machine learning methods. Emphasis is placed on image and text recognition. Through hands-on projects, students become familiar with the theory underlying algorithms for classification, clustering, regression, and optimization, as well as reinforcement learning and other topics in machine learning. As part of the course, students develop their own programs in Python.

1. 6.036 Lecture Notes (2020) - https://phillipi.github.io/6.882/2020/notes/6.036_notes.pdf 2. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. (2024). Mathematics for Machine Learning. Cambridge University Press. – 417 p. 3. MITx: Machine Learning with Python: from Linear Models to Deep Learning – online course (in English) – https://www.edx.org/learn/machine-learning/massachusetts-institute-of-technology-machine-learning-with-python-from-linear-models-to-deep-learning 4. Big Data 101 – online course (in English) – www.bdu.intela-edu.com/courses/course-v1:BigDataUniversity+BD0101EN+v2/about 5. Shai Shalev-Shwartz, Shai Ben-David. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. – 449 p. 6. Senio, P. S. Theory of Probability and Mathematical Statistics: Textbook. – Kyiv, Center for Educational Literature, 2004. – 448 pp.

Lectures, practical sessions, and independent study.

In English