Where: Building 19; Hall 1;2&3
Credit: 12
Description
Machine Learning (ML) models are used in various applications, affecting societies directly or indirectly in daily life. The Machine learning (ML) field connects computer science and statistics, allowing computers to use collected data or past experience to solve unseen problems. It’s a must-have skill for every analyst and data scientist. It is also an important skill for anyone who wants to draw conclusions and make scientific suggestions based on collected raw data. A wide range of successful ML applications exist, including systems to predict customer behavior, support decision-making, recognize faces or spoken speech, optimize robot behavior, and extract knowledge from biological data. During this Workshop, participants will learn how to write and train ML models that can help in their field of study. Attendees will have the chance to learn the basics of Python and how to use it to build machine-learning models. In this Workshop, we will use Google Colab as our IDE, but students are free to use their favorite IDE.
All workshop materials will be uploaded on this website: (https://qahtanaa.github.io/wep_mlb_24/).
Abdulhakim Qahtan
Abdulhakim Qahtan is an assistant professor at the Data Intensive Systems (DIS) Group, Information and Computing Sciences Department. Before joining Utrecht University, he worked as a Postdoctoral Researcher at Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Qatar (2016-2019).
Dr. Qahtan earned his PhD degree from the Machine Intelligence & kNowledge Engineering (MINE) Lab at King Abdullah University of Science and Technology (KAUST) (2016). He completed his B.S. and M.S. in Computer Science at Cairo University, Egypt and King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, respectively. He worked as a teaching assistant at Taiz University, Yemen and a lecturer at KFUPM, Saudi Arabia.
His current research focuses on data cleaning, data stream mining, time series analysis, algorithmic fairness and explainable machine learning.
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