Description

Machine Learning is one of the fundamental subjects in the field of Artificial Intelligence. Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., learning to recognize images or speech, classifying text documents, detecting credit card frauds, or driving autonomous vehicles). This course covers both fundamental theory, practical algorithms and the applications for machine learning from a variety of perspectives. It includes a range of topics, from supervised learning (such as Naïve Bayes Classifier, Linear Regression, Logistic Regression, and Neural Networks) and their applications, to unsupervised learning (such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)) and their applications, and from traditional (shallow) learning (such as Support Vector Machine (SVM)) to recent state-of-the-art deep learning methods (such as Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN)). The course is intended to prepare students for basic understanding of machine learning fundamentals and equip students with the capability to apply machine learning techniques through real world business applications (to solve real world problems). NOTE: This is an algorithm and technical course, and it is highly recommended that students are proficient in programming, probabilities, statistics, linear algebra and calculus. Solid math background will be very useful and helpful for your learning journey. It is highly recommended that students have taken IS424 Data Mining and Business Analytics first if you do not have such math background knowledge. Having taken IS424 first will make this difficult course, Machine Learning & Applications, easier.

Learning Outcomes

Graduate Learning Outcomes

Disciplinary Knowledge, Multidisciplinary Knowledge, Interdisciplinary Knowledge, Critical thinking & problem solving, Innovation and enterprising skills, Collaboration and leadership, Communication, Intercultural understanding and sensitivity, Understanding of global and Asian perspectives, Ethics and social responsibility, Understanding of sustainability issues, Self-directed learning

Competencies

Data Analytics, Applications Development, Cloud Computing, Algorithm Analysis, Data Strategy