1. SylabUZ
2. Faculty of Exact and Natural Sciences
3. winter term 2019/2020
4. computer science and econometrics - First-cycle studies leading to Bachelor's degree
5. Basics of Machine Learning

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Basics of Machine Learning - course description

Aim of the course

The assumption of the course is to familiarize students with the basic machine learning algorithms that are currently very widely used in the practical analysis of various types of databases.

The aim of the course is to acquire by the student the ability to select appropriate machine learning methods depending on the practical business problem.

Analyzes of real data will be carried out using R software, which is currently very popular among analysts. After this course, the student will have the ability to use specialized R program libraries to solve specific problems using machine learning algorithms.

Prerequisites

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Basic knowledge of statistics and probability theory.

Scope

Lecture / Laboratory:

1. Introduction to machine learning. Basic data mining tasks.
2. Data preprocessing: data cleaning, variable transformations, graphical presentation of variables and their dependencies
3. Classification of machine learning methods. Supervised and unsupervised learning methods. Training and test data sets.
4. Cluster analysis algorithms: hierarchical grouping, K-means method.
5. Quality assessment of clustering results.
6. Dimension reduction methods: principal components analysis.
7. Classification algorithms: decision trees, Bayesian network.
8. Regression, statistical classification models: linear and logistic.
9. Assessment of the quality of classification models: confusion matrix, ROC curve, classification accuracy.
    

 

Teaching methods

Lectures: traditional or online form.

Laboratory: solving research problems with the use of machine learning algorithms with the use of specialized libraries of the R program. Discussion on possible solutions to the given problems. Teamwork.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

1. Checking the level of preparation of students and their activity both in the laboratory and during the lecture.

2. The laboratory grade will be issued on the basis of the test results and / or projects.

Recommended reading

1. Lantz B., Machine learning with R. PACKT 2013.

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. J. Koronacki, J. Ćwik: Statystyczne systemy uczące się. Wydanie drugie, EXIT, Warszawa, 2007

Further reading

1. D.T. Larose, Metody i modele eksploracji danych, Wydawnictwo naukowe PWN, Warszawa, 2012.
2. M. Gągolewski, Programowanie w języku R – Analiza danych,  obliczenia, symulacje, Wydawnictwo naukowe PWN, Warszawa, 2016.
3. T. Górecki Podstawy statystyki z przykładami w R BTC Legionowo 2011.

Notes

Modified by dr Alina Szelecka (last modification: 21-11-2020 06:10)