كلية الهندسة - جامعة عين شمس, الرئيسية
Machine Learning and Pattern Recognition
What Will Learn?
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Course Aims
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Course Goals
- Decent Work and Economic Growth
- Industry, Innovation and Infrastructure
- Sustainable Cities and Communities
Requirements
PHM113s AND CSE231s
Description
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English Description
Fundamental concepts for machine learning and pattern recognition: Theories, Algorithms. Data exploration and wrangling. Bayesian decision theory. Parametric and non-parametric learning. Classifier design. Data reduction, Data clustering. Principal component analysis. Boosting techniques. Support Vector Machines (SVMs). Deep learning with neural networks. Applications areas: Computer vision, Speech recognition, Data mining, Statistics, Information retrieval, Bioinformatics. -
Arabic Description
Fundamental concepts for machine learning and pattern recognition: Theories, Algorithms. Data exploration and wrangling. Bayesian decision theory. Parametric and non-parametric learning. Classifier design. Data reduction, Data clustering. Principal component analysis. Boosting techniques. Support Vector Machines (SVMs). Deep learning with neural networks. Applications areas: Computer vision, Speech recognition, Data mining, Statistics, Information retrieval, Bioinformatics.
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قسمهندسة الحاسبات والنظم
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الساعات المعتمدة2
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الدرجاتالإجمالي ( 100 ) = نصف العام (20) + tr.Student Activities (30 = tr.Industry 0% , tr.Project 10% , tr.Self_learning 0% , tr.Seminar 20% ) + درجة الامتحان (50)
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الساعاتساعات المحاضرة: 2, ساعات التعليم: 1, ساعات المعمل: 0
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Required SWL100
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Equivalent ECTS4
- 1) Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
- 2) Introduction to machine learning, Alpaydin, 2020 - Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006. 2) Introduction to machine learning, Alpaydin, 2020 - Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006. 2) Introduction to machine learning, Alpaydin, 2020.