Dc070498
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Causal Analysis in Data Science Summaries
Summaries for both modules of the course "Causal Analysis in Data Science" at Tilburg University.
- Package deal
- • 2 items •
Summaries for both modules of the course "Causal Analysis in Data Science" at Tilburg University.
Causal Analysis in Data Science: Multilevel Analysis Module
Extensive summary of the module 'Multilevel Analysis' (Causal Analysis in Data Science course). It contains many notes from the video lectures. All video lectures are discussed in detail.
- Package deal
- Summary
- • 21 pages •
Extensive summary of the module 'Multilevel Analysis' (Causal Analysis in Data Science course). It contains many notes from the video lectures. All video lectures are discussed in detail.
Causal Analysis in Data Science: Conditional Process Analysis
All components of the module 'conditional process analysis' are discussed (Causal Analysis in Data Science course): 'Mediation', 'Moderation' and 'Moderated Mediation'. Summary is comprehensive. It contains many notes from the video lectures.
- Book & Paket-Deal
- Summary
- • 16 pages •
All components of the module 'conditional process analysis' are discussed (Causal Analysis in Data Science course): 'Mediation', 'Moderation' and 'Moderated Mediation'. Summary is comprehensive. It contains many notes from the video lectures.
Data Mining Final Exam Summary
Useful summary for the Data Mining course at Tilburg University. All parts of the course are discussed, except the guest lecture. The summary describes and explains the elementary principles of data mining.
- Summary
- • 20 pages •
Useful summary for the Data Mining course at Tilburg University. All parts of the course are discussed, except the guest lecture. The summary describes and explains the elementary principles of data mining.
Summary Deep Learning 2020
Summary for Tilburg University's Deep Learning course (academic year 2019-2020). All 6 lectures are discussed: introduction, Multi-Layer Perceptron, Convolutional Neural Networks, Optimizers and Regularization, Sequence Modeling and Generative Models.
- Summary
- • 21 pages •
Summary for Tilburg University's Deep Learning course (academic year 2019-2020). All 6 lectures are discussed: introduction, Multi-Layer Perceptron, Convolutional Neural Networks, Optimizers and Regularization, Sequence Modeling and Generative Models.
Midterm Data Mining Summary (lectures)
In this document you will find all important information about the theory that was discussed during the first three lectures. The document also contains additional explanations for various concepts. It is a good preparation for the midterm.
- Summary
- • 9 pages •
In this document you will find all important information about the theory that was discussed during the first three lectures. The document also contains additional explanations for various concepts. It is a good preparation for the midterm.
Theory questions about Machine Learning material
- Flashcards
- • 37 items •
Exam preparation: Statistics and Methodology
Ideal preparation for the 'Statistics and Methodology' course. Contains 50 practice questions regarding the theory and a very comprehensive summary. Summary contains many examples and extra notes.
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- • 2 items •
- Statistics and Methodology Summary • Summary
- Sample exam questions for Statistics and Methodology • Flashcards
Ideal preparation for the 'Statistics and Methodology' course. Contains 50 practice questions regarding the theory and a very comprehensive summary. Summary contains many examples and extra notes.
Preparation for Final: Data Science Regulation & Law
Prepare for the final exam of 'Data Science Regulation & Law'. This bundle contains a summary and some practice questions about the theory that is discussed in block 2.
- Package deal
- • 2 items •
Prepare for the final exam of 'Data Science Regulation & Law'. This bundle contains a summary and some practice questions about the theory that is discussed in block 2.
Theory questions Data Science Regulation & Law Block 2
- Flashcards
- • 27 items •
Summary Deep Learning 2020
Machine Learning: Comprehensive Summary
Machine Learning: Comprehensive Summary
Statistics and Methodology Summary
Summary Language and Thought