|
Lecturer(s)
|
-
Fürst Tomáš, RNDr. Ph.D.
-
Pavlačka Ondřej, RNDr. Ph.D.
|
|
Course content
|
1. Machine Learning - introduction, types of problems. 2. Regression - linear, logistic, multivariate, Gradient Descent method for estimating parameters. 3. Validation of model - underfitting, overfitting, regularization, cross-validation. 4. Artificial neural networks: biological motivation, feed forward NN and backpropagation. 5. Support Vector Machines. 6. Decision trees. 7. Recommender systems.
|
|
Learning activities and teaching methods
|
|
Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
|
|
Learning outcomes
|
Understanding machine learning methods Ability to implement machine learning methods
Understanding machine learning methods Ability to implement machine learning methods
|
|
Prerequisites
|
linear algebra, calculus, programming, English language
KMA/MA1 and KAG/LA1A and KMA/BAY
|
|
Assessment methods and criteria
|
Dialog, Seminar Work
Oral exam. Credits: apply ML methods to a given data set
|
|
Recommended literature
|
-
Bishop, Ch. M. (2011). Pattern recognition and machine learning.
-
Bishop, Ch.M., Bishop, H. (2024). Deep Learning: Foundations and Concepts.
-
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Sebastopol.
-
Hastie, T., Tibshirani, R., Friedman, J. (2016). The Elements of Statistical Learning.
-
Chollet, F. (2021). Deep Learning with Python.
-
Raschka, S., Mirjali, V. (2019). Python Machine Learning. Birmingham.
|