Machine and Deep Learning Methods in Python 0800-UCZMASZ
Data Science programming in Python (Pandas, NumPy, Scikit-Learn, TensorFlow/Keras)
Statistical Data Analysis (estimation and hypothesis testing, regression, Bayesian Inference)
Machine Learning methods (classification, clustering and dimensionality reduction)
Algorithms of Linear and Logistic Regression (Decision Trees, Random Forests, Support-Vector Machines (SVM), naive Bayes, K-nearest neighbor, Bagging, XGBoost, PCA, k-means, DBSCAN)
Deep Learning (Neural Networks) for image classification (computer vision)
Neural Networks for text classification (Natural Language Processing (NLP))
SPARK as a big data analysis tool
Total student workload
Learning outcomes - social competencies
Teaching methods
Expository teaching methods
- programmed material
Exploratory teaching methods
- laboratory
- brainstorming
- project work
- practical
Prerequisites
Course coordinators
Assessment criteria
The final grade will be a weighted average of the grades earned for (1) active participation in class (40%) and the final Jupyter notebook project (60%).
Grading criteria:
- 0-49% grade 2,
- 50-59% grade 3,
- 60-69% grade 3+,
- 70-79% grade 4,
- 80-89% grade 4+,
- 90-100% grade 5.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: