Bootcamp – introduction to machine learning 1000-317bBUM
The lecture has the form of an intensive course taught during the first two weeks of the first semester. The following topics are covered:
1. Objective function, division test vs validation
2. Feature engineering
3. Overfitting, regularization
4. Introduction to linear and logistic regression
5. K nearest neighbours algorithm
6. Data exploration and visualization. Histogram, density function visualization, box plot.
Type of course
Course coordinators
Learning outcomes
Knowledge: the student
* has based in theory and well organized knowledge of fundamental techniques of machine learning and methodology of constructions and research in this field [K_W06].
Abilities: the student is able to
* employ basic techniques of machine learning to plan and conduct the study of properties of solutions [K_U08];
* visualize the results of studies in machine learning [K_U09].
Social competences: the student is ready to
* critically evaluate acquired knowledge and information [K_K01];
* recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution [K_K02].
Assessment criteria
Final test and programming assignment with grades
Bibliography
1. Trevor Hastie, Robert Tibshirani, Jerome H., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, Berlin
2. Andrew Ng, Machine Learning Yearning, https://www.deeplearning.ai/machine-learning-yearning/
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: