Chapters from a textbook that introduces the main topics of Buddhist Philosophy will be read and analyzed in the seminars. Each student will thereafter individually examine a particular problem of Buddhist Philosophy.

This course gives a brief overview of the basic concepts, principles and practice of data science. The main goal is to learn to plan and carry out a simple practical data science project. The course covers the main methods for descriptive data analysis and visualization, frequent pattern mining, cluster analysis, principal components analysis, common methods of machine learning for classification and regression (including deep neural networks), managing data and interpreting results of statistical tests. The main stages of data science projects are dicussed and available software tools reviewed. The homeworks are to be solved using the programming language Python 3 and its libraries.

The course will provide a practical introduction to the basic concepts and approaches of data science and business analytics. The main emphasize will be on the practical application along with the introduction of the necessary R packages and interpretation of results.

The lectures and computational lab sessions will cover the necessary engineering aspects for designing diffractive optical elements for generating optical fields for various applications.

Image formation Image acquisition Digitalization Image manipulation Illumination enhancement Transformation Frequency domain image processing Denosing Colour processing Object recognition

The course focuses on competences, skills and know-how that are relevant to all of the first-year students in the program, regardless of specialization. The topics covered include the planning and organization of studies in the Social Science PhD program, including the curriculum, its structure, requirements and options, individual study plans, and periodic progress reviews; the rights and obligations of PhD students and junior researchers; the process of planning, writing, submitting and defending a doctoral dissertation; the pros and cons of a monograph-style and article-based dissertation; supervision practices, co-supervision arrangements, issues related to co-authorship and ways to solve problems in the supervisor-supervisee relationship; the importance of feedback and criticism in research, and the ways to provide and receive feedback. The course provides an overview of the Estonian publication classification system and explains which publications meet the publication requirements for a doctoral dissertation at the UT. It will also cover the essentials of publishing in the social sciences, from considerations relevant to the choice of a journal to preparing and submitting manuscripts to responding to reviews and interacting with the editors. The course will also cover the basics of research dissemination and popularization, from conference participation to sharing one's research results with the media, via online networking sites and the social media. It will also focus on the fundamentals of research ethics, and the principles of open and responsible research. The course offers an introduction to applying for research funding and managing grant projects, focusing on the main sources of funding and types of grants before offering advice on proposal writing and sharing info on relevant support services at the UT. The course concludes with a discussion on how to survive and thrive as a PhD student and with sharing tips on time and self-management.