Course Details

Course Details
Course No. : 2504051530
Course Name : Machine Learning
Prerequisite : 110405311 و 110406260
Credit  : 3
Theory credit : 3
Practical Credit : 0
Course Discription : An introductory course in machine learning (ML) that covers the basic theory, applications, and algorithms. ML enables computational systems to learn and find relationships/solutions from observed data. This course balances the theory with the practice. ML has many important applications in data-driven engineering, economics, and analysis of big data. ML is indeed a hot topic. Topics covered in this course include understanding types of learning (supervised/unsupervised/reinforcement); linear classification/regression; model selection and validation; overfitting; the theory of generalizations; training vs testing; least squares; basics in optimization theory/algorithms; neural networks; support vector machines (SVM); kernel-methods; Perceptron Learning Algorithm (PLA); and similarity-based methods (KNN, and Radial-Basis Functions (K-means clustering)).
Syllabus : Download
The Course offering during the Second Semester-2025/2026
Section NOTheory SectionTeacherTime & Location
10 DR SA'ED AHMAD QWAITEEN ALAJLOUNI 11,30 - 13,00 ن ر / المادة تدرس بشكل مدمج في مبنى الهندسة هـ2023 / على منصة(مايكروسوفت teams)