Workshop on Interpretable Machine Learning Data Using R
Statistics Undergraduate Program at the Faculty of Mathematics and Natural Sciences, University of Bengkulu, organized a workshop titled “Interpretable Machine Learning Data Using R” on November 15, 2023. The event took place at the Statistics Laboratory and was exclusively designed for undergraduate students pursuing their S1 degree in Statistics. Led by esteemed expert Arie Vatricia, M.T.I., Ph.D., the workshop delved into the intricacies of interpreting machine learning data using the programming language R. Dr. Vatricia’s vast expertise and extensive knowledge in the field offered participants a comprehensive understanding of the practical applications and methodologies involved in interpreting complex data using R.
The workshop aimed to equip S1 Statistics students with valuable insights into the interpretability of machine learning models using R, a widely-used programming language for statistical computing and graphics. Dr. Arie Vatricia, an accomplished figure in the realm of data science and machine learning, guided the participants through various methodologies and techniques essential for comprehending and analyzing intricate data models. The engaging session fostered an interactive learning environment, allowing attendees to gain hands-on experience and practical knowledge crucial for their academic and professional growth in the field of statistics and data analysis.
By hosting this workshop, the Statistics Undergraduate Program at the University of Bengkulu reaffirmed its commitment to providing students with opportunities to engage with industry-leading experts and gain practical insights into emerging trends and technologies in the field of statistics and data science. The workshop not only offered a platform for theoretical learning but also empowered participants to explore the practical aspects of interpreting machine learning data using R under the expert guidance of Dr. Arie Vatricia, ultimately enhancing their skill set for future endeavors in the data-driven landscape.
