Students are introduced to the concepts of developing machine learning models to interpret software engineering datasets. Students explore open source libraries for solving linear regression, non-linear regression, classification,clustering and dimensionality reduction problems focusing their applications in software engineering lifecycle datasets. Students learn the deep-learning archtitectures including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Students gain hands-on experience solving complex and simple software engineering lifecycle problems related to coding, testing and quality assurance, requirements etc., by applying various machine learning and deep learning algorithms. Prerequisites:SENG 3120 with a minimum grade of C
Students are introduced to the concepts of developing machine learning models to interpret software engineering datasets. Students explore open source libraries for solving linear regression, non-linear regression, classification,clustering and dimensionality reduction problems focusing their applications in software engineering lifecycle datasets. Students learn the deep-learning archtitectures including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Students gain hands-on experience solving complex and simple software engineering lifecycle problems related to coding, testing and quality assurance, requirements etc., by applying various machine learning and deep learning algorithms. Prerequisites:SENG 3120 with a minimum grade of C
