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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Machine Learning 2

The course is not on the list Without time-table
Code Completion Credits Range Language
01SU2 Z,ZK 4 2P+2C Czech
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Mathematics
Synopsis:

1.Fundamental topics from the probability theory and machine learning (classical distributions, Bayes’ theorem, Kullback-Leibler divergence, curse of dimensionality, overfitting, maximum likelihood and maximum a posteriori estimators, Principle Component Analysis)

2.Decision trees: general schema, recursive partitioning, optimal partitioning and pruning, ensemble learning - bagging, boosting, random forests.

3.Examples of decision trees: Adaptive boosting – AdaBoost, Gradient boosting, Xgboost.

4.Numerical methods for optimization (steepest descent, conjugate gradient, Newton and quasi-Newton, constrained extrema, Lagrangian).

5.Deep feedforward networks (hidden units, nonlinear activation functions, output units, loss functional, stochastic gradient descent, back-propagation algorithm)

6.Optimization for training deep models (regularization, algorithms with adaptive learning rates)

7.Convolutional neural networks

8.Recurrent neural networks

9.Advanced network architectures (autoencoders, Generative Adversarial networks)

10.Applications of deep learning (classification, segmentation, image reconstruction)

Requirements:
Syllabus of lectures:

visit https://su2.utia.cas.cz/

Syllabus of tutorials:

visit https://su2.utia.cas.cz/

Study Objective:
Study materials:

Key references:

[1] Goodfellow I., Bengio Y., Courville A.: Deep Learning, MIT Press, 2016.

[2] Bishop, Christopher M.: Pattern Recognition and Machine Learning. Springer, 2006.

Recommended references:

[3] Géron A: Hands-On Machine Learning with Scikit-Learn and TensorFlow, 2017.

[4] Chollet, F.: Deep Learning with Python, 2018.

[5] online resources: pytorch.org/tutorials/, playground.tensorflow.org, tensorflow.org/learn/

Note:
Further information:
https://su2.utia.cas.cz/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-05-23
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