Webinar 4: Working With Data

This session covers critical data preprocessing steps essential for machine learning model performance and accuracy. Trainees will learn techniques for handling missing values, such as imputation and flagging, and encoding categorical and real-valued data into numerical formats. The session will include normalization and standardization to improve gradient-based algorithms’ convergence rates, and data preparation methods like one-hot encoding and representation. Understanding the hypothesis space and managing biases through cross-validation are key topics. The session will also cover partitioning data into training, validation, and test sets, and optimizing model performance through parameter tuning with grid or random search.

Sarah Alotaibi is an Assistant Professor in the Department of Computer Science at King Saud University. She holds a B.Sc. and M.Sc. in Computer Science from King Saud University and a Ph.D. in Computer Vision from the University of York in the United Kingdom. Her research focuses on computer vision and machine learning, with an interest in deep learning with statistical and appearance modeling, face modeling, reflectance analysis, and inverse rendering. Dr. Alotaibi has published numerous papers in these areas.