Neurale Netwerken
The Neural Networks course introduces second-year BSc Artificial Intelligence students to the fundamental principles and architectures underlying modern neural computation. The course begins with foundational concepts such as perceptrons, multi-layer perceptrons, and the backpropagation algorithm. It then develops the student’s understanding of deep learning architectures including convolutional neural networks, recurrent neural networks, and transformers. In
Introduction to Machine Learning 1
The core of this course revolves around programming machine learning algorithms for yourself, as a way to truly understand what exactly they are learning. Each of the different modules will focus on programming a different algorithm, understanding the math required for that algorithm, and discussing a philosophical question or a societal impact related to applying
Biologie van Planten
Aan de hand van een aantal deelhoofdstukken van de boeken Life, the Science of Biology en Plant Physiology and Development wordt de studenten inzicht gegeven in de bouw en het functioneren van planten. De exacte (deel)hoofdstukken zullen in de studiewijzer bekend worden gemaakt. Allereerst worden het plantenrijk en de hoofdlijnen van de evolutie van planten
Biotic Interactions
This course provides knowledge on recent developments in research on plant-pathogen and plant-insect interactions. This will include the molecular targets and signal transduction pathways involved, and the ecological aspects of biotic interactions in nature and in agriculture. With regard to defense against pathogens, the innate immune response and the gene-for-gene model will be discussed in
Abiotic Stress
Environmental (abiotic) stress is the most important limiting factor in crop productivity. This master course aims to broaden the student’s horizon in abiotic-stress biology and to gain the latest insights into the molecular mechanisms by which plants perceive such stress signals, how they are transduced, and how stress signals are eventually converted into intracellular responses
Introduction to Machine Learning 2
In this course we continue with discussing machine learning models and algorithms. While the focus in Introduction to Machine Learning 1 was on programming basic models yourself, in this course we will make more use of libraries for the elementary parts and the focus will mainly be on how to combine these parts into more
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
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
Fairness, Accountability, Confidentiality and Transparency in AI
Artificial Intelligence and data science are increasingly being used to power decision making in settings that are highly consequential for people and society. Algorithmic decision making promises great benefits across sectors and domains. At the same time, there are many high-profile stories about the potential harms of algorithms, for individuals, population groups, and society at