Incoterms مصطلحات التجارة الدولية

Deep Learning Applications (2026-2027)

الهدف من مادة تطبيقات التعلم العميق هو إطلاع الطالب على مجال التعلم العميق وتطبيقاته في العلوم الاقتصادية والتجارية وعلوم التسيير، باستخدام مكتبة Keras. يتعلم الطالب تصميم الشبكات العصبية (MLP وCNN وLSTM وGRU) وتدريبها وتقييمها، لبناء نماذج تنبؤية وتصنيفية تدعم اتخاذ القرار.

L'objectif du module « Applications du Deep Learning » est d'initier l'étudiant au deep learning et à ses applications en économie, commerce et gestion, à l'aide de Keras. Il apprendra à concevoir, entraîner et évaluer des réseaux de neurones (MLP, CNN, LSTM, GRU) pour construire des modèles de prévision et de classification qui aident à la prise de décision.

The objective of the "Deep Learning Applications" course is to introduce students to deep learning and its applications in economics, business and management, using Keras. Students will learn to design, train and evaluate neural networks (MLP, CNN, LSTM, GRU) in order to build predictive and classification models that support decision-making.

Statistical programs 2

Introduction to Statistical Software

In today's world, data and information have become the backbone of decision-making across various scientific and practical fields. With the continuous growth in the volume and complexity of data, it has become essential to use specialized tools to analyze these data and extract valuable insights. Statistical software plays a critical role in this context, allowing researchers, academics, and practitioners to handle large and complex data sets in an accurate and efficient manner.

Statistical software like SPSS, R, and SAS are among the most widely used tools in various fields, including economics, marketing, social sciences, and medical sciences. These programs enable users to conduct different types of statistical analyses such as regression analysis, hypothesis testing, cluster analysis, and many other techniques that support data-driven decision-making.

This course aims to introduce students to the most important statistical software and how to use them in data analysis, providing practical guidance on the steps and procedures necessary to perform statistical analyses. It empowers students to interpret results and apply them in their academic research