التعلم الآلي في الأمن السيبراني

This course covers essential topics in machine learning applied to cybersecurity. Beginning with an introduction to machine learning, linear regression, and classifiers, students progress to logistic regression, k-nearest neighbors, and error metrics evaluation. Advanced topics include statistical hypothesis testing, gradient descent techniques, decision trees, random forests, and support vector machines. Additionally, students explore probabilistic classification, basics of neural networks, linear algebra for machine learning, cybersecurity fundamentals, intrusion detection, malware analysis, and advanced applications like reinforcement learning in cybersecurity.