1. SylabUZ
2. Wydział Nauk Inżynieryjno-Technicznych
3. semestr zimowy 2023/2024
4. Informatyka - Program Erasmus drugiego stopnia
5. Machine learning

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Machine learning - opis przedmiotu

Cel przedmiotu

• Familiarize students with the concept of machine learning and its applications to the analysis of large data sets included in social media, ERP systems and modern e-business applications.
• Teach students how to select the appropriate data analysis techniques depending on the scale of the problem under consideration and the type of analysis (real-time, batch mode, data stream processing).
• Teach students to work using modern programming languages and platforms directed towards machine leaning, such as Python, R and JMP.

Wymagania wstępne

Zapisz zmiany

• Fundamentals of probability and engineering statistics
• Basics of numerical methods
• Basic programming skills in Python

Zakres tematyczny

Linear classification methods: supervised classification; linear discriminant analysis; discrimination based on linear regression and logistic regression; model diagnostics.

Classification based on probability distributions: Bayesian classifier and maximum likelihood; optimality of the Bayes rule; practical synthesis of classifiers.

Classification based o nonparametric estimation of probability distributions: estimation of distributions within classes; nearest neighbor rule.

Decision tress and families of classifiers: partition rules; trimming rules; algorithms of bagging and boosting; random forests.

Regression analysis: global parametric models; nonparametric regression; random effects and mixed linear models.

Generalizations of linear methods: elastic discrimination; support vector machines.

Projection methods and detection of hidden varables: unsupervised learning systems; principal component analysis; factor analysis; multidimensional scaling.

Cluster analysis: combinatorial metods; hierarchical methods.

Deep learning: unidirectional deep networks; regularization; convolution networks; recurrent networks.

Metody kształcenia

conventional lecture, discussion, laboratory classes

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

• Lecture - the passing criterion is a sufficient mark from the final exam.
• Laboratory - the passing criterion are positive marks for all laboratory exercises.
• Final mark components = lecture: 50% + laboratory: 50%

Literatura podstawowa

1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, 2nd Edn, Springer, 2021
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Second Edition, Springer, 2009
3. Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. MIT Press, Cambridge, MA, 2016
4. Brian Steele, John Chandler, Swarna Reddy: Algorithms for Data Science, Springer, 2016

Literatura uzupełniająca

 

Uwagi

Zmodyfikowane przez prof. dr hab. inż. Dariusz Uciński (ostatnia modyfikacja: 22-04-2023 13:14)