Deep neural networks

General data

Course ID:	1000-2M16GSN
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Deep neural networks
Name in Polish:	Głębokie sieci neuronowe (wspólne z 1000-317bDNN)
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science Specific programme courses of 2nd stage Bioinformatics
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Prerequisites (description):	Proficiency in Python programming.
Short description:	The goal of the course is to show use cases for deep neural networks. During the course state-of-the-art techniques, algorithms and tools will be presented. Among others two main blocks of the course will concern image classification and text processing.
Full description:	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).
Bibliography:	Online books http://neuralnetworksanddeeplearning.com/ http://www.deeplearningbook.org/
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 methods and 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.

This course is not currently offered.

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