Infopaket

Machine Learning in Earth Sciences

ВК 2.8.2

Вибіркова дисципліна для ОП

First

2023/2024

7 Semester

3

Students should know: basic graph search algorithms; fundamentals of Bayesian networks, Markov models, machine learning, reinforcement learning, and neural networks; main Python modules for organizing artificial intelligence systems. Students should be able to: develop their own intelligent systems based on the studied concepts and algorithms; utilize existing artificial intelligence technologies in their products and solutions; independently select machine learning tools in Python (TensorFlow, Keras, NumPy, Pandas); apply acquired skills in projects related to the field of Earth sciences.

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

This discipline is an elective in the professional training cycle for bachelor’s students under the "Big Data Analysis in Earth Sciences" block. The course covers both theoretical and practical foundations of artificial intelligence and machine learning using Python. Key concepts and algorithms underlying modern artificial intelligence are studied and explored, with attention given to image and text recognition. Through practical projects, students engage with the theory behind algorithms for graph search, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning. Ethical considerations in the application of machine learning methods are also addressed. As part of the course, students develop their own programs in Python.

Lectures, practical sessions, consultations, independent work

Ukrainian