Deep neural networks 1000-2M16GSN
1.Introduction to machine learning (2 lectures): what machine learning is, supervised and unsupervised learning, regression, classification, loss function. Linear and logistic regression. Regularization, optimizing hyperparameters, judging the quality of a model.
2.Introduction to neural networks (2 lectures): introducing pytorch, how to initialize a neural network, activation functions, regularization, optimizing loss function.
3.Convolutional netural networks (4-5 lectures): image classification, benchmark datasets (MNIST, CIFAR, ImageNet), data augmentation, convlolutions, pooling, basic architectures.
4.Recurrrent neural networks (2-3 lectures): LSTM, text processing.
5.Possible futher topics depending on students' preferences (3-4 lectures).
Type of course
Prerequisites (description)
Learning outcomes
Knowledge
* Knows the basics of machine learning.
* Understands the learning algorithms for neural networks.
* Knows basic architectures of convolutional and recurrent neural networks.
Abilities
* Is able to use a chosen modern library of machine learning procedures.
* Can implement image classification algorithms based on convolutional neural networks.
* Can implement text processing algorithms based on recurrent neural networks.
* Can use English at the proficiency level B2+ of Common European Framework of Reference for Languages, with particular emphasis on the computer science terminology
Competences
* Is ready to critically evaluate acquired knowledge and information.
* Is ready to recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution.
* Is ready to think and act in an entrepreneurial way.
Assessment criteria
Final grade is based on the sum of scores for the project, homework assignments and final exam. Homework assignment consists of a few programs to be implemented.
Bibliography
Online books
http://neuralnetworksanddeeplearning.com/
http://www.deeplearningbook.org/
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: