Workshops

Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Large Language Models (LLMs) – how they work, and how they can be used in bioinformatics applications (text mining, information extraction) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)Students will gain experience in: Applications and Limitations of Machine Learning and Deep Learning Decision Trees and Random Forests – how they work, how they are coded in Python and R, and how they can be used in bioinformatic applications (biomarker discovery and modeling) Artificial Neural Networks (ANNs) – how they work, how data is encoded, how they are coded in Python and R, and how they can be used in bioinformatic applications (classification and secondary structure prediction) Large Language Models (LLMs) – how they work, and how they can be used in bioinformatics applications (text mining, information extraction) Using Machine Learning tools (Decision Trees, ANNs and HMMs) on the Web (SciKit Learn and Keras/Colab)