Introduction to Data science and AI
7,5 credits (ECTS)
During the course, a wide selection of methods for Data Science and AI will be introduced.
The course is divided into three parts:
Introduction to data science
- Implementation of data science solutions, using Python, basic data analysis and visualization.
- Introduction of the data science process, and appropriate methodology.
- Examples of core data science methods with case studies such as in clustering, classification and regression.
- Data science put in context regarding ethics, regulations and limitations
Statistical methods for data science and AI
- Introduction of some common stochastic models with examples of applications indata science and AI (for instance, naive Bayes classifiers, topic models for text and Hidden Markov Models for sequence data).
- Introduction to classical AI and machine learning, including the relationship torelated areas such as algorithms and optimization, and AI philosophy.
- Examples of methods and applications of AI, in classical AI (search and constraint satisfaction), and ML-based (search engines, naive Bayes and neural networks)
- Discussion of ethics and societal impact of AI.