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Machine Learning and Pattern Recognition

What Will Learn?

  • Course Aims
  • Course Goals
  • Decent Work and Economic Growth
  • Industry, Innovation and Infrastructure
  • Sustainable Cities and Communities

Requirements

PHM113s AND CSE231s

Description

  • 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.
  • Department
    Computer and Systems Engineering
  • Credit Hours
    2
  • Grades
    Total ( 100 ) = Midterm (20) + tr.Student Activities (30 = tr.Industry 0% , tr.Project 10% , tr.Self_learning 0% , tr.Seminar 20% ) + Exam Grade (50)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 1, Lab Hours: 0
  • Required SWL
    100
  • Equivalent ECTS
    4
  • 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.