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Lecturer(s)
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Pavlačka Ondřej, RNDr. Ph.D.
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Course content
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1. Machine learning - types of problems, data preprocessing, SW tools - Python libraries (ScikitLearn, Keras, TensorFlow), Matlab toolboxes. 2. Convolutional neural networks. 3. Ensemble learning - idea, selected methods (e.g. Random forests). 4. Recommender system. 5. Reinforcement learning 6. Anomaly detection.
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Learning activities and teaching methods
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Lecture, Demonstration
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Learning outcomes
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The aim is to undarstand mathematical background of advanced methods from the area of machine learning, and to be able to employ them for solving real problems using special packages in Python.
Understanding advanced machine learning methods Ability to implement advanced machine learning methods
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Prerequisites
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linear algebra, calculus, programming, English language
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Assessment methods and criteria
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Student performance, Seminar Work
Colloquium: The student shows his/her ability to apply the presented methods to solve the given problems.
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Recommended literature
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Bishop, Ch. M. (2011). Pattern recognition and machine learning.
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Cielen D., Meysman A., Ali M. (2016). Introducing Data Science: Big Data, Machine Learning, and more, using Python tools. Manning Publications.
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Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd Ed.). O´Reilly, Sebastopol.
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Müller, A. C., Guido, S. (2017). Introduction to Machine Learning with Python. O´Reilly, Sebastopol.
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Raschka, S., Mirjali, V. (2019). Python Machine Learning (3rd Ed.). Packt Publishing, Birmingham.
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S. Theodoridis. (2020). Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
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Tibshirani, H. T., Jerome, R. F. (2016). The Elements of Statistical Learning. Springer.
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