Experimental Design and Data Analysis of Breeding Trials
In this course, students are taught principles of experimental design of trials and statistical analysis of trial data with a special emphasis to linear and generalized linear methods, mixed models, analysis of multi-environment trials using different statistical methods
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
Modeling and Data Analysis in Complex Networks
Big Data is mostly obtained from features of components and the interactions between components in large complex systems. Examples are (1) end user features and interactions in both online and real-world social networks like Twitter, LinkedIn (2) data from content sharing platforms such as YouTube (3) physiological data of the brain and (4) stock prices
Practical Computing for Life Scientists
This course offers a practical introduction to advanced computer use for the analysis of biological data. It is focused on technical aspects of handling large data files, working on remote computers running the Linux operating system using the command line (shell), and developing practical programming/scripting skills. There will be some emphasis on biological molecules (DNA,
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
Basic Machine Learning for Bioinformatics
Modern biology increasingly relies on vast datasets generated through genomic, transcriptomic, and single-cell omics techniques. To make sense of these large-scale data and extract meaningful patterns or predictions, machine learning (ML) has become essential. This course introduces students to the foundational principles and techniques of ML as applied to biological data. Students learn the differences
Advanced Biotechnology (UCU)
This is one of the final courses in a series of three courses in molecular cell biology. In the first introductory course (UCSCIBIO11), you gained a lot of basic knowledge about the major characteristics of life on Earth. In the intermediate UCSCIBIO21 course, we explored the molecular world of the cell in more detail and
Systems Analysis and Modelling
This course introduces students to systems approaches for analysing ecological and agro-ecosystems through quantitative simulation models. Students learn to formulate and specify dynamic models, evaluate model performance using statistical methods and inverse modelling, and simulate spatial processes using partial differential equations. Topics include population and nutrient dynamics, dispersal, vegetation patterning, and model calibration. Programming is
Statistical Learning
Statistical learning provides a probabilistic and statistical understanding of topics in machine learning. The overarching goal of the course is to develop methods for estimating (or `learning’) an unknown function from data or making predictions for unseen function outputs. The course aims to empower the student to make a justified decision in adopting machine learning
Gene Technology
Recombinant DNA technology has brought about a revolution in our understanding of molecular processes in living organisms. To date, there is no field in experimental biology that has remained untouched by the potential of isolating, analyzing and manipulating genes and organisms. Thus, gene technology provides essential tools in both fundamental and applied medical, industrial, agricultural,