Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu Machine and Deep Learning:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów —
Profil
Moduł —
Przedmiot Machine and Deep Learning
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Przetwarzania Sygnałów i Inżynierii Multimedialnej
Nauczyciel odpowiedzialny Adam Krzyżak <Adam.Krzyzak@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 7,0 ECTS (formy) 7,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny — Grupa obieralna —

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW1 45 4,00,56zaliczenie
projektyP1 30 3,00,44zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Basic knowledge of Matlab or Mathcad environments
W-2Basic knowledge about programming
W-3Basic knowledge of linear algebra, probability and statistics

Cele przedmiotu

KODCel modułu/przedmiotu
C-1This course is intended to present a unified approach to machine learning techniques and algorithms and their applications in practical problems.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
projekty
T-P-1Students prepare individual project with the requirements given by the teacher.30
30
wykłady
T-W-1Classification2
T-W-2Generative vs. discriminative learning2
T-W-3Naive Bayes2
T-W-4Gaussian discriminant analysis2
T-W-5Linear models: linear and polynomial regression2
T-W-6L2 and L1 regularization2
T-W-7Sparse models, logistic regression2
T-W-8Non-linear models: decision trees, instance-based learning, random forest, adaboost, shallow neural networks4
T-W-9Support vector machines and kernels2
T-W-10Computational learning theory2
T-W-11Unsupervised learning: clustering2
T-W-12K-means, mixture models, density estimation, expectation maximization2
T-W-13Autoencoder, PCA2
T-W-14Structured models: graphical models, Bayes nets. Learning in dynamical systems: Hidden Markov Models and other types of temporal/sequence models. Approximate inference. Gibbs sampling. Deep belief learning.4
T-W-15Survey of deep neural network architectures2
T-W-16Convolutional neural networks4
T-W-17Recurrent neural networks, attention, transformers4
T-W-18Generative adversarial networks (GANs)3
45

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
projekty
A-P-1Participation in classes30
A-P-2Generating projects' reports.45
75
wykłady
A-W-1Participation in classes45
A-W-2Reading relevant literature.55
100

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Traditional lecture. Students prepare individual projects and reports.

Sposoby oceny

KODSposób oceny
S-1Ocena formująca: Written exam (test) / project work

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WE_1-_??_W01
Knowledge of basic machine learning algorithms. Ability to implement some machine learning algorithms in chosen environment (e.g. Matlab).		——C-1T-W-1, T-W-6, T-W-7, T-W-9, T-W-10, T-W-11, T-W-13, T-W-14, T-W-12, T-W-8, T-W-5, T-W-4, T-W-3, T-W-2M-1S-1

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WE_1-_??_U01
Students will get the skills about creating algorithms related to the machine learning theory and also ability to implement some machine learning algorithms in chosen environment (e.g. Matlab).		——C-1T-W-1, T-W-6, T-W-7, T-W-9, T-W-10, T-W-11, T-W-13, T-W-14, T-W-12, T-W-8, T-W-5, T-W-4, T-W-3, T-W-2M-1S-1

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
WM-WE_1-_??_W01
Knowledge of basic machine learning algorithms. Ability to implement some machine learning algorithms in chosen environment (e.g. Matlab).		2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
WM-WE_1-_??_U01
Students will get the skills about creating algorithms related to the machine learning theory and also ability to implement some machine learning algorithms in chosen environment (e.g. Matlab).		2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
  2. Yoshua Bengio, Aaron Courville and Ian Goodfellow, Deep Learning, MIT Press, Cambridge, Massachusetts, USA, 2016
  3. Aurelian Geron, Hands-on Machine Learning with Scikit-Learn, Keras & TensortFlow, O'Reilly, Sebastopol, CA, USA, 2023, 3
  4. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, Cham , Switzerland, 2023
  5. Christopher Bishop and Hugh Bishop, Deep Learning, Springer, Cham, Switzerland, 2024

Literatura dodatkowa

  1. Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning Second Edition, Springer, 2009
  2. Garreth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning, Springer, 2013
  3. Ethem Alpaydin, Introduction to Machinning Second Edition, MIT Press, 2009
  4. Tom Mitchell, Machine Learning, McGraw-Hill, 1997

Treści programowe - projekty

KODTreść programowaGodziny
T-P-1Students prepare individual project with the requirements given by the teacher.30
30

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Classification2
T-W-2Generative vs. discriminative learning2
T-W-3Naive Bayes2
T-W-4Gaussian discriminant analysis2
T-W-5Linear models: linear and polynomial regression2
T-W-6L2 and L1 regularization2
T-W-7Sparse models, logistic regression2
T-W-8Non-linear models: decision trees, instance-based learning, random forest, adaboost, shallow neural networks4
T-W-9Support vector machines and kernels2
T-W-10Computational learning theory2
T-W-11Unsupervised learning: clustering2
T-W-12K-means, mixture models, density estimation, expectation maximization2
T-W-13Autoencoder, PCA2
T-W-14Structured models: graphical models, Bayes nets. Learning in dynamical systems: Hidden Markov Models and other types of temporal/sequence models. Approximate inference. Gibbs sampling. Deep belief learning.4
T-W-15Survey of deep neural network architectures2
T-W-16Convolutional neural networks4
T-W-17Recurrent neural networks, attention, transformers4
T-W-18Generative adversarial networks (GANs)3
45

Formy aktywności - projekty

KODForma aktywnościGodziny
A-P-1Participation in classes30
A-P-2Generating projects' reports.45
75
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Participation in classes45
A-W-2Reading relevant literature.55
100
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WE_1-_??_W01Knowledge of basic machine learning algorithms. Ability to implement some machine learning algorithms in chosen environment (e.g. Matlab).
Cel przedmiotuC-1This course is intended to present a unified approach to machine learning techniques and algorithms and their applications in practical problems.
Treści programoweT-W-1Classification
T-W-6L2 and L1 regularization
T-W-7Sparse models, logistic regression
T-W-9Support vector machines and kernels
T-W-10Computational learning theory
T-W-11Unsupervised learning: clustering
T-W-13Autoencoder, PCA
T-W-14Structured models: graphical models, Bayes nets. Learning in dynamical systems: Hidden Markov Models and other types of temporal/sequence models. Approximate inference. Gibbs sampling. Deep belief learning.
T-W-12K-means, mixture models, density estimation, expectation maximization
T-W-8Non-linear models: decision trees, instance-based learning, random forest, adaboost, shallow neural networks
T-W-5Linear models: linear and polynomial regression
T-W-4Gaussian discriminant analysis
T-W-3Naive Bayes
T-W-2Generative vs. discriminative learning
Metody nauczaniaM-1Traditional lecture. Students prepare individual projects and reports.
Sposób ocenyS-1Ocena formująca: Written exam (test) / project work
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WE_1-_??_U01Students will get the skills about creating algorithms related to the machine learning theory and also ability to implement some machine learning algorithms in chosen environment (e.g. Matlab).
Cel przedmiotuC-1This course is intended to present a unified approach to machine learning techniques and algorithms and their applications in practical problems.
Treści programoweT-W-1Classification
T-W-6L2 and L1 regularization
T-W-7Sparse models, logistic regression
T-W-9Support vector machines and kernels
T-W-10Computational learning theory
T-W-11Unsupervised learning: clustering
T-W-13Autoencoder, PCA
T-W-14Structured models: graphical models, Bayes nets. Learning in dynamical systems: Hidden Markov Models and other types of temporal/sequence models. Approximate inference. Gibbs sampling. Deep belief learning.
T-W-12K-means, mixture models, density estimation, expectation maximization
T-W-8Non-linear models: decision trees, instance-based learning, random forest, adaboost, shallow neural networks
T-W-5Linear models: linear and polynomial regression
T-W-4Gaussian discriminant analysis
T-W-3Naive Bayes
T-W-2Generative vs. discriminative learning
Metody nauczaniaM-1Traditional lecture. Students prepare individual projects and reports.
Sposób ocenyS-1Ocena formująca: Written exam (test) / project work
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0