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Objectives and competences

The objective of this course is to comprehensively present the knowledge about the development of intelligent systems with machine learning, and to train students for state-of-the-art machine learning approaches used in science and industry.

Content (Syllabus outline)

• Introduction: data science, artificial intelligence and machine learning, fields of use, software tools and frameworks. • Intelligent information solutions: programming from scratch, integrated solutions for data scientists, machine learning as a service. • Machine learning process: basic methods and tasks, data collection and processing, feature selection, transformation and feature creation, evaluation of knowledge models. • Ensemble methods: combining machine learning methods, bagging, boosting, stacking, Random Forests, Gradient Boosting. • Deep learning and neural networks, convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks. • Setting and optimization of machine learning parameters. • Arranging machine learning projects. • Case studies of efficient intelligent solutions from various domains.

Learning and teaching methods

• lectures, • case studies, • computer tutorials, • project.

Intended learning outcomes - knowledge and understanding

Knowledge and understanding: On completion of this course the student will be able to • understand the applicability of various machine learning methods, • choose and use the appropriate machine learning method and prepare the data to solve a given task, • use ensemble methods and neural networks to solve real-world problems, • properly set and optimize machine learning parameters, • plan, perform and lead an intelligent solution development project, • analyse and evaluate specific intelligent solutions and/or machine learning based systems.

Intended learning outcomes - transferable/key skills and other attributes

• Communication skills: writing the technical report. • Use of information technology: use of program tools and environments for the development of intelligent machine learning solutions. • Problem solving: systematic approach to problem solving with the use of intelligent methods, intelligent data analysis. • Working in a group: participation in a project team.

Readings

• A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow – Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd edition, O'Reilly Media, 2019. • G. Bonaccorso, A. Fandango, R. Shanmugamani: Python: Advanced Guide to Artificial Intelligence – Expert machine learning systems and intelligent agents using Python, Packt Publishing, 2018. • F. Chollet: Deep Learning with Python, Manning Publications, 2017. • A. Thakur: Approaching (Almost) Any Machine Learning Problem, Abhishek Thakur, 2020. • J. Hearty: Advanced Machine Learning with Python: Solve data science problems by mastering cutting-edge machine learning techniques in Python, Packt Publishing, 2016.

Prerequisits

None.

  • red. prof. dr. VILI PODGORELEC, univ. dipl. inž. rač. in inf.

  • Project work: 30
  • 1st midterm examination: 25
  • 2nd midterm examination: 25
  • work in computational laboratory: 10
  • lab work test: 10

  • : 30
  • : 30
  • : 120

  • Slovenian
  • Slovenian

  • INFORMATICS AND DATA TECHNOLOGIES - 1st