Administracja Centralna Uczelni - Wymiana międzynarodowa (S2)
Sylabus przedmiotu Machine Learning:
Informacje podstawowe
| Kierunek studiów | Wymiana międzynarodowa | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | drugiego stopnia |
| Tytuł zawodowy absolwenta | |||
| Obszary studiów | — | ||
| Profil | |||
| Moduł | — | ||
| Przedmiot | Machine Learning | ||
| Specjalność | przedmiot wspólny | ||
| Jednostka prowadząca | Katedra Metod Sztucznej Inteligencji i Matematyki Stosowanej | ||
| Nauczyciel odpowiedzialny | Przemysław Klęsk <pklesk@wi.zut.edu.pl> | ||
| Inni nauczyciele | |||
| ECTS (planowane) | 5,0 | ECTS (formy) | 5,0 |
| Forma zaliczenia | zaliczenie | Język | angielski |
| Blok obieralny | — | Grupa obieralna | — |
Formy dydaktyczne
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | mathematics |
| W-2 | algorithms and data structures |
| W-3 | programming |
| W-4 | probability calculus and statistics |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | Developping a general understanding about data analysis and machine learning methods. |
| C-2 | Building the understanding about learning from data. |
| C-3 | Familiarization with probabilistic, tree-based, and boosted classifiers, and the related algorithms. |
| C-4 | Familiarization with rules mining and related algorithms. |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| laboratoria | ||
| T-L-1 | Programming PCA in MATLAB. | 2 |
| T-L-2 | Programming CART trees in MATLAB. | 2 |
| T-L-3 | Programming SVM optimization tasks (several versions) in MATLAB. | 2 |
| T-L-4 | Programming MARS algorithm in MATLAB. | 2 |
| T-L-5 | Programming the naive Bayes classifier (MATLAB) - for 'wine data set' (in class) and a selected data set (homework). | 6 |
| T-L-6 | Programming the Apriori algorithm - mining association rules. | 4 |
| T-L-7 | Programming an exhaustive generator of decision rules (for given premise length). | 4 |
| T-L-8 | Programming the CART algorithm - building a complete tree. | 4 |
| T-L-9 | Programming heuristics for pruning CART trees. | 4 |
| 30 | ||
| wykłady | ||
| T-W-1 | Principal Component Analysis (PCA) as a method for dimensionality reduction. Review of notions: variance, covariance, correlation coefficient, covariance matrix. Minimization of projection lengths of data points onto a given direction. Derivation of PCA. Interpretation of eigenvalues and eigenvectors. | 4 |
| T-W-2 | Decision trees - CART algorithm. Impurity functions, greedy generation of a complete tree. Pruning heuristics for decision trees (depth-based, leaves-based). | 4 |
| T-W-3 | Support Vector Machines (SVM). Distance of data points from the decision hyperplane. Separation margin. Formulation of the SVM optimization task without and with Lagrange multipliers. Support vectors - what are they? Soft-margin SVM and related optimization tasks. SVMs with non-linear decision boundary using the kernel trick. | 5 |
| T-W-4 | Multivariate Adaptive Regression Splines (MARS) for approximation tasks. Construction of splines. Least-squares approximation with arbitrary bases (in particular MARS splines). Learning algorithm. Similarities to CART. | 2 |
| T-W-5 | Review of some elements of probability calculus. Derivation of Naive Bayes classifier. Remarks on computational complexity with and without the naive assumption. Bayes rule. LaPlace correction. Beta distributions. | 4 |
| T-W-6 | Mining association rules by means of Apriori algorithm. Support and confidence measures. Finding frequent sets (induction). Rules generation mechanics. Remarks on the hashmap data structure applied for Apriori algorithm. Pareto-optimal rules. Remarks on decision rules generation. | 4 |
| T-W-7 | Decision trees and CART algorithm. Impurity functions and their properties. Best splits as minimizers of expected impurity of children nodes. CART greedy algorithm. Tree pruning heuristics (by depth, by penalizing number of leafs). Recursions for traversing the subtrees (greedy and exhaustive). | 3 |
| T-W-8 | Ensemble methods: bagging and boosting (meta classifiers). AdaBoost algorithm. Exponential criterion vs zero-one-loss function. Real boost algorithm. | 2 |
| T-W-9 | Exam. | 2 |
| 30 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| laboratoria | ||
| A-L-1 | Participation in lab classes. | 30 |
| A-L-2 | Programming homework assignments. | 10 |
| A-L-3 | Programming homework tasks. | 30 |
| A-L-4 | Preparation for short tests (15 min) carried out in lab classes. | 5 |
| 75 | ||
| wykłady | ||
| A-W-1 | Participation in lectures. | 30 |
| A-W-2 | Preparation for the exam. | 18 |
| A-W-3 | Sitting for the exam. | 2 |
| 50 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | Lecture. |
| M-2 | Computer programming. |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena formująca: Four short tests (15 minutes long) at the end of each topic during the lab. |
| S-2 | Ocena formująca: Four grades for the programs written as homeworks. |
| S-3 | Ocena podsumowująca: Final grade for the lab calculated as a weighted mean from partial grades: - tests (weight: 40%), - programs (weight: 60%). |
| S-4 | Ocena podsumowująca: Final grade for lectures from the test (2 h). |
Zamierzone efekty uczenia się - wiedza
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WI_1-_null_W01 Student posesses an elementary knowledge on machine learning algorithms and techniques of data analysis. | — | — | C-1 | T-W-4, T-W-3, T-W-9, T-W-2, T-W-1, T-L-4, T-L-1, T-L-2, T-L-3 | M-1 | S-4 |
| WM-WI_1-_null_W02 Student has an elementary knowledge on data mining algorithms and notions. | — | — | C-4, C-3, C-2 | T-W-9, T-W-5, T-W-6, T-W-7, T-W-8, T-L-8, T-L-5, T-L-6, T-L-7, T-L-9 | M-1 | S-4 |
Zamierzone efekty uczenia się - umiejętności
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| WM-WI_1-_null_U01 Student can implement (in Python or MATLAB) several machine learning algorithms and techniques. | — | — | C-1 | T-W-4, T-W-3, T-W-9, T-W-2, T-W-1, T-L-4, T-L-1, T-L-2, T-L-3 | M-2 | S-2 |
| WM-WI_1-_null_U02 Student can implement (MATLAB or Python) data mining algorithms presented during lectures. | — | — | C-4, C-3, C-2 | T-W-9, T-W-5, T-W-6, T-W-7, T-W-8, T-L-8, T-L-5, T-L-6, T-L-7, T-L-9 | M-2 | S-2 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WI_1-_null_W01 Student posesses an elementary knowledge on machine learning algorithms and techniques of data analysis. | 2,0 | |
| 3,0 | Obtaining at least 50% of points in the final test. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WI_1-_null_W02 Student has an elementary knowledge on data mining algorithms and notions. | 2,0 | |
| 3,0 | Obtaining at least 50% of points in the final test. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Kryterium oceny - umiejętności
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| WM-WI_1-_null_U01 Student can implement (in Python or MATLAB) several machine learning algorithms and techniques. | 2,0 | |
| 3,0 | Obtaining a positive average grade from homework programming tasks. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 | ||
| WM-WI_1-_null_U02 Student can implement (MATLAB or Python) data mining algorithms presented during lectures. | 2,0 | |
| 3,0 | Obtaining a positive average grade for homework programming projects. | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Literatura podstawowa
- M. J. Zaki, W. Meira Jr, Data Mining and Analysis - Fundamental Concepts and Algorithms, Cambridge University Press, 2014
- M. J. Zaki, W. Meira Jr, "Data Mining and Analysis - Fundamental Concepts and Algorithms", Cambridge University Press, 2014
- P. Klęsk, Electronic materials for the course available at: http://wikizmsi.zut.edu.pl, 2015