1. SylabUZ
2. Faculty of Exact and Natural Sciences
3. winter term 2022/2023
4. computer science and econometrics - Second-cycle studies leading to MS degree
5. Machine Learning

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Machine Learning - course description

Aim of the course

The aim of the course is to prepare students to solve practical problems using both classical statistical models
neural network algorithms and other known machine learning methods. An important goal of this subject is to develop reasoning skills,
analytical thinking and selection of appropriate machine learning algorithms for a given problem.

 Analyzes of real data will be performed using one of the two programs most often used by analysts: R or Python. After this course
the student will have the ability to use specialized libraries of the selected program to solve specific problems using
machine learning algorithms.

Prerequisites

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Knowledge of the basics of statistics and probability theory. Statistical data analysis.

Scope

Lecture/Lab:
  1. Introduction to machine learning. Machine learning methods as a decision support technique.
  2. Classification of machine learning methods. Supervised and unsupervised learning methods.
  3. Data preprocessing and scaling. Cross-validation.
  4. Unsupervised learning. Cluster analysis algorithms: hierarchical clustering, K-means method. Dimensionality reduction.
  5. Association rules.
  6. Supervised learning techniques. Classification and regression algorithms: linear regression model, logistic model, decision trees, artificial neural networks.
  7. Assessment of the quality of models. Learning curves.

Teaching methods

Lecture: traditional and problem-based, multimedia presentation.

 Laboratory: the laboratory program includes deepening the issues discussed during lectures. Solving research problems using machine learning algorithms
using specialized R or Python libraries. Teamwork. Discussion related to the use of appropriate algorithms and interpretation of intermediate and final results.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Checking the degree of students' preparation and their activity both in the laboratory and during the lecture.

The skills acquired during laboratory classes are verified on the basis of the student's performance of specified tasks and problems settled in the form of reports.
The passing threshold is 50% of the total points.

Recommended reading

1. Geron Aurelien: Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow. Helion 2020.
2. T. Morzy, Eksploracja danych – metody i algorytmy, Wydawnictwo naukowe PWN, Warszawa, 2013.
3. M. Szeliga, Data science i uczenie maszynowe, Wydawnictwo naukowe PWN, Warszawa, 2017.
4. Christopher M. Bishop Pattern Recognition and Machine Learning, Springer, 2007.
5. S. Raschka, V. Mirjalili, Python. Uczenie maszynowe. Helion, 2019.
6. W. Richert, L.P. Coelho, Building Machine Learning Systems with Python, Packt Publishing, 2013.
7.  A. C. Muller, S. Guido: Machine learning, Python i data science. Wprowadzenie. Helion, 2021.

Further reading

1. J. Koronacki, J. Ćwik: Statystyczne systemy uczące się. Wydanie drugie, EXIT, Warszawa, 2007.
2.  D.T. Larose, Metody i modele eksploracji danych, Wydawnictwo naukowe PWN, Warszawa, 2012.
3. M. Gągolewski, Programowanie w języku R – Analiza danych,  obliczenia, symulacje, Wydawnictwo naukowe PWN, Warszawa, 2016.
4. T. Górecki Podstawy statystyki z przykładami w R BTC Legionowo 2011.

Notes

Modified by dr Magdalena Wojciech (last modification: 18-02-2024 19:02)