The objective of this course is to introduce students to Machine Learning techniques based on a unified, probabilistic approach. The course will review regression, classification, and clustering machine-learning models. In addition, the course will introduce neural networks, mixture models, reinforcement learning, and federated learning methods. Students will get hands-on experience with machine learning from a series of practical engineering case studies. Python-based machine learning libraries will be used.
The objective of this course is to introduce students to the fields of Machine Learning and Data Science. Through this course, the students will learn various algorithms and how they are implemented to solve real-world problems. Students will get hands-on experience in implementing these algorithms using various programming languages and platforms.
This course is intended to give an in-depth look at the implementation and test phases of the software construction process. This is a project-based course that requires completing a medium scale project at the end of the term. Topics covered in this course include an introduction to Software development process, basic process models, software specification, introduction to software design, programming language specifics (C#), code review and inspections, testing, building and debugging tools (Unity) and version control (Git). This course will use C# as the language and Unity as the graphics platform.