Introduction to Bioinformatics for Life Sciences
Description: “Introduction to Bioinformatics for Molecular Biologists” is a joint course for the various life science Masters programs at the Utrecht University. This introductory course provides an overview of the importance of bioinformatics in various biological disciplines. While a biological background is required, no programming skills are needed. The course can be considered a general
Data Science Ethics Colloquium Series
Ethical and legal considerations are essential to many applications of data science. This colloquium series is intended to make the students aware of such considerations and to give them the vocabulary to discuss those matters with experts of legal and ethical aspects. Furthermore, the colloquium will enable students to develop an understanding of professional integrity
Applied Plant Biology
This course consists of 4 modules. Information/insights obtained within previous modules will be implemented in later ones. Module 1: Masterclasses by guest-lecturers from the green sector; academia, industry, finance and (semi)governmental organizations. Teaching/learning format: Lectures + discussion, writing assignment Module 2: Identifying current challenges in applied molecular Plant biology. Teaching/learning format: Project presentation + discussion
Plant-Environment Interactions
General lectures on plant-environment interactions will set the stage for an advanced-level course on how plants adjust to their environmental factors. The case studies discussed in this interactive course include drought, temperature, salinity, flooding, and nutrient stresses. Students will independently study the latest developments in plant responses and plasticity towards variations in their environment. The
Philosophy of AI (UvA)
In this course, we approach (the philosophy of) artificial intelligence (AI) from the perspective of theoretical philosophy. The course focuses on the discussion of the intelligence of artificial intelligence, in particular against the backdrop of the debate between nativists and empiricists. We will do so, following Cameron Buckner’s very recent book From Deep Learning to
Bioinformatics and Genomics
In this course, attention is paid to understanding and working with large amounts of data as has been obtained in recent years with genetic and molecular research. These technological developments require new skills and concepts to be able to understand and conduct life science research. Successively work with mutations and sequencing data will be used.
Fundamentals of Data Science
Data science is a dynamic and fast-growing interdisciplinary research field that, across science, industry, and government, is altering how people understand the world and make decisions. Not surprisingly, the demand for data science skills is on the rise. This course will cover key principles and tools of data science. In particular, the course will cover
Machine Learning 1
This course is lecture based, with homework assignments and programming assignments. The curriculum is based on chapters 1,2,3,4,5,6,7,9,14 of the book Pattern Recognition and Machine Learning by C. Bishop: Statistical learning principles; Linear regression; Linear classification; Neural networks; Kernel methods; Dimensionality reduction; Clustering methods; Ensemble methods
Plant-Microbe Interactions
Plant-microbe interactions will be introduced in a general lecture, highlighting recent developments and the importance of the reviewing process. For a set of recent manuscripts the students will act as reviewers and editors following the format provided by high standard international journals. Editor decisions will be presented and discussed.
Algorithms for sequence-based Bioinformatics
After having followed this course, the student has a good understanding of algorithms and data structures in genomics used for DNA sequence analysis. The student is able to implement algorithms in python, and can translate methods described in scientific literature into a working implementation. Bioinformatics analyses in genomics aim to compare large sets of genomes