Bootcamp – introduction to machine learning

elective monographs

The goal of the course is to present the set of elementary notions of machine learning necessary to understand contemporary, advanced techniques of machine learning as well to instil the programming techniques necessary to efficiently use them.

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.

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/

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].

Final test and programming assignment with grades