Computer vision has been the driving force behind creating a multi-billion-dollar market with many fundamental applications including face recognition, manufacturing control, autonomous driving, and medical image processing. The core technology behind much of this success has of course been deep learning, which has pushed the capabilities of what computers can “see” and “create” even further. In this course, I will provide an introduction into the core methods, algorithms, and applications in modern computer vision. I will introduce the basics of computer vision with a focus on implementation and experimentation and includes pointers to current research, covering topics from low-, to mid-, to high-level computer vision approaches.
The course goals are:
To get an understanding of core concepts in computer vision
To implement these core concepts in Python and later in OpenCV yourself
To get a glimpse into current research on select topics
This course teaches the basics of neural networks with a focus on implementation and experimentation in order to build a strong foundation for understanding how neural networks learn from data. It covers topics the basics of Perceptrons, Linear Layers [fully-connected], Convolutional Neural Networks [CNNs], Recurrent Networks [Markov, LSTMs], all the way up to Transformers.
The course goals are:
To develop an understanding of neural networks
To implement basic algorithms and benchmark different architectures in numpy and pytorch
To test the architectures on several benchmark datasets
This course builds on prior knowledge, covering the latest topics in neural networks with a focus on systems geared towards visual and vision-language analysis. In each segment of this course, we will focus on one major architecture in the broad scope of neural networks. Each segment will be headed by an inspirational paper that will provide the backbone of our discussion. From there, we will branch out into other, current research works, which will be presented and discussed in class. You will have the opportunity to suggest research you are interested in for discussion in class as well!
The course goals are:
To push our understanding further of architectural advances in neural networks
To read, present, and discuss current research on select topics
To test the architectures on several benchmark datasets
This course reviews the latest developments in the fast-paced field of AI applications in healthcare. Guiding questions are: What does it mean when the AI says that there is evidence for breast cancer? Is this reliable? Who regulates this? We will read the latest papers covering general overviews of the field, specific novel approaches to (multi modal) analysis of healthcare data, as well as regulation and ethical issue arising from the use of AI in the healthcare domain.
The course goals are:
To review topics and papers related to AI in healthcare
To augment the basic understanding with state-of-the-art studies
To get an insight view on a real-world AI healthcare startup
A good grasp of mathematics lies at the core of any career in the natural sciences - this is especially true for neuroscience, cognitive science, and artificial intelligence in which vast amounts of data need to be analyzed efficiently in order to come up with viable models and predictions. After a brief math refresher, this course gives an introduction into applied mathematics (also called "scientific computing") covering exploratory data analysis (EDA), numerical methods for solving linear equations, function minimization and approximation, Fourier analysis, differential equations, as well as the basic foundations of neural networks and deep learning. It also works through a set of examples, and gives you the opportunity to design, implement, and present a small applied math course project of your own.
The course goals are:
To get an understanding of some core concepts in scientific computing & EDA
To connect theoretical with practical knowledge
To form connections between mathematical/computational topics and neuroscience
To get an introduction into programming with Python
To design, implement, and present a course project
The brain is one of the most complex systems that science is currently trying to understand. In addition, the associated disciplines of neuroscience and cognitive science are one of the hottest areas in science right now. In this course, I will provide an introduction into the core concepts underlying brain and cognitive science. We will cover the structure of the brain (hardware), its perceptual apparatus (input), its motor functions (output), and then continue on to describe higher-level, cognitive functions (such as attention, memory, language, emotion, and decision making).
The course goals are:
To get an understanding of core concepts in brain and cognitive science
To get a glimpse into current research on select topics
To read, understand, and summarize a few current research papers in the form of well-structured essays
Experiments are at the core of the scientific process – through a careful balance of theory and empirical evidence theories are formed and rebutted in experimental settings. This course will focus on the philosophy of experimental design and give a high-level introduction to thinking about and analyzing of experiments. We will then proceed to discuss (in)famous experiments in science in general and in psychology and neuroscience in particular and analyze their impact on today’s worldview – including theoretical, practical, and ethical implications. The experiments are chosen to cover a wide range of questions about the human existence – from the way the brain perceives color, to how we form beliefs, to how humans cooperate. The goal of this course is to teach you about the way science is conducted and above all to sharpen your critical and creative intellect.
The course goals are:
To get an understanding of core concepts in experimental design
To connect design and analysis and to learn the basics of experimental analysis
To get an overview of classic experiments about aspects of human existence
To stimulate critical reading and creative thinking