Special Topics

Course Description

Machine learning is the field dedicated to enabling computers to learn from data and execute tasks without explicit programming instructions. Its widespread adoption is so ingrained in modern life that we often utilize it unknowingly. This course offers an introduction to machine learning methodologies, encompassing classic supervised and unsupervised algorithms, all geared towards harnessing data for predictive analysis. Throughout this course you will acquire proficiency in utilizing no-code/low-code environments, such as KNIME, for constructing, training, and deploying machine learning workflows to tackle real-world predictive challenges. It is an excellent choice for both beginners and intermediate learners interested in comprehending machine learning concepts and data engineering. By the course’s conclusion, you should have a firm grasp of machine learning fundamentals without the need for coding or extensive mathematical calculations. Although no prior knowledge of machine learning or programming is required, students should possess a basic understanding of calculus, probability theory, and linear algebra. Prerequisites MIS 311, CQS 311 Credits: 4

Code

MIS 480J

Credits

4

Subject

MIS