1. SylabUZ
2. Wydział Matematyki, Informatyki i Ekonometrii
3. semestr zimowy 2022/2023
4. Mathematics - pierwszego stopnia z tyt. licencjata
5. Basics of Machine Learning

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Basics of Machine Learning - opis przedmiotu

Cel przedmiotu

The aim of the course is to introduce students to machine learning models and algorithms, with an emphasis on their practical applications. The student will also become acquainted with statistical methods and algorithms aimed at discovering patterns and rules hidden in data.

The ultimate goal of the course is for the student to acquire the ability to choose the appropriate tools and machine learning methods depending on the practical problem at hand. Special attention will be paid to interpreting the results obtained in the context of the research problem.

After this course, the student will have the skills to use specialized Python libraries dedicated to solving specific problems using machine learning algorithms.

Wymagania wstępne

Zapisz zmiany

Knowledge of basic Statistics and Probability Theory.

Zakres tematyczny

Lecture/Laboratory

1. Introduction to the problematics of machine learning. Data preprocessing: cleaning data, variable transformations, graphical presentation of variable distributions.

2. Classification of basic machine learning methods. supervised and unsupervised learning methods.

3. Cluster analysis algorithms: hierarchical clustering, K-means method. Evaluation of clustering results quality.

4. Dimension reduction method: principal components analysis.

5. Classification algorithms: LDA, decision trees, k nearest neighbors, Bayesian network.

6. Introduction to artificial neural networks.

7. Assessment of the quality of classification models: confusion matrix, ROC curve, classification accuracy.

8. Combined algorithms: boosting and bagging methods, random forests.

 

Metody kształcenia

Lecture: traditional and problem-based.

Laboratory: solving research problems using machine learning algorithms using specialized Python libraries. Discussion. Teamwork.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

The grade for the laboratory will be based on the results from the colloquium and/or projects (80%) and activity in classes (20%).

Literatura podstawowa

1. S. Raschka, V. Mirjalili, Python Uczenie maszynowe. Helion, 2019.
2. W. Richert, L. P. Coelho, Building Machine Learning Systems with Python. Pact Publishing, 2013.
3. P. Dangeti, Statistics for Machine Learning: Techniques for Exploring Supervised, Unsupervised and Reinforcement Learning Models with Python and R., Packt Publishing, 2017.
4. Geron Aurelien: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, O'Reilly, 2019.

Literatura uzupełniająca

1. GoodFellow I, Bengio Y., Courville A. Deep learning. Systemy uczące się, PWN, Warszawa, 2018.
2. W. Richert, L.P. Coelho, Building Machine Learning Systems with Python, Packt Publishing, 2013

Uwagi

Zmodyfikowane przez dr hab. Bogdan Szal, prof. UZ (ostatnia modyfikacja: 28-03-2024 21:56)