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Lecturer(s)
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Fürst Tomáš, RNDr. Ph.D.
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Pavlačka Ondřej, RNDr. Ph.D.
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Course content
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1. Machine Learning - introduction, types of problems. 2. Regression - linear, logistic, multivariate, Gradient Descent method for estimating parameters. 3. Validation of model - underfitting, overfitting, regularization, cross-validation. 4. Artificial neural networks: biological motivation, feed forward NN and backpropagation. 5. Support Vector Machines. 6. Decision trees. 7. Recommender systems.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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Understanding machine learning methods Ability to implement machine learning methods
Understanding machine learning methods Ability to implement machine learning methods
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Prerequisites
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linear algebra, calculus, programming, English language
KMA/MA1 and KMA/BAYES and KAG/LA1A
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Assessment methods and criteria
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Student performance, Analysis of Creative works (Music, Pictorial,Literary)
Colloquium: Active participation. The student shows his ability to chose a ML algorithm for a data set, train the model, diagnose it a present the results before an audience.
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Recommended literature
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(2009). Data Mining, Inference, and Prediction. Second Edition, Springer.
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Online přednáška.
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Online přednáška.
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Online přednáška.
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Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome. (2016). The Elements of Statistical Learning. Springer.
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Christopher Bishop. (2011). Pattern recognition and machine learning. Springer.
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