Practical Machine Learning in Python

optional courses

The aim of the course is to present the latest machine learning methods, using Python programming language. In order to fully illustrate the machine learning issues, all three types of analysis will be presented: unsupervised, supervised and reinforcement learning. However, the main focus will be on predictive models, both classification and regression.

Classes will focus on the formation of an intuitive understanding of the algorithms discussed, their strengths and weaknesses and the acquisition of practical skills to use them.

•Introduction to machine learning

◦Data science, Data Mining, Deep Learning, Big Data and Machine learning.

◦Machine learning for business

◦Machine learning as a function

•Supervised learning

◦Regression and classification. Objective function.

◦Bias variance dillema.

◦Linear regression and logistic regression

◦Decision tress.

◦Simple decision trees.

◦Random Forest.

◦Boosting.

◦Neural networks.

•Unsupervised learning

◦Clustering.

◦Not only K-Means: comparison of different clustering methods.

◦Curse of multidimensionality

◦Dimensions reduction.

▪Principal component analysis (PCA)

▪Self-organizing map (SOM)

▪t-distributed stochastic neighbour embedding (t-SNE)

•Reinforcement Learning

Harrington, Peter. Machine learning in action. Vol. 5. Greenwich, CT: Manning, 2012

Knowledge

Student knows methods of predictive modelling. Student knows and understands methods based on decision trees and neural networks. Student knows the sources of obtaining large data sets. Student knows methods of using Python for data analysis. Student knows applications of presented statistical methods and can create market analyses at work or for the needs of his own company.

Skills

Student can choose appropriate modelling method for a given problem. On the basis of acquired knowledge, student can formulate his/her own opinion and apply the theoretical knowledge to description and analysis of economic phenomena. Student can look for and find data sets, apply predictive modelling and prepare description of performed analysis.

Social skills

The practice of using Python programming language allows to increase the skills of independent learning and increases the competences in object-oriented programming. The exercises and modelling practices carried out during the course allow students to be critical of the results obtained in scientific work.

Final project (60%)

Exam (test) (40%)

An extra possibility to pass the course is to participate and achieve a good result in the data analysis competition (e.g. kaggle). Details will be given at the first meeting.