Goal: The aim of the course is to provide a practical approach to the basic concepts and
processes of data science. Through real-life application examples from practice,
students will gain precise theoretical and practical hands-on knowledge by
experiencing the material in depth. Machine learning algorithms form the backbone of
the theoretical knowledge, while practical exercises provide a practical representation
of the theory through the use of the Python language.
Course description: Python basic, data cleaning, data visualization, data preprocessing, supervised,
unsupervised learning, overfitting, underfitting, model validation,
learning/validation/testing sets, cross-validation, Bias-Variance, least squares, Linear
Regression, Gradient Method, Maximum-likelihood estimation, Logistic regression,
Learning/validation/testing set, Cross-validation, Bias-Variance tradeoff, PrecisionRecall, F1-score, ROC curve, SVM, Neural networks, Decision trees, Random forests,
Boosting, Unsupervised learning, Clustering, K-means clustering, Reinforcement
learning.
Lecture schedule