This seminar introduces graduate students to the vast, exciting, and ever-growing research area of machine learning. Both historical and current research papers on the subject will be presented and discussed to cultivate a trajectory-based perspective of the field. Furthermore, the course adopts a “model-based machine learning” (C. Bishop) perspective and emphasis: the application should inform the selection and development of a particular machine learning model (i.e., is application-focused). Both supervised (learning from data with a target outcome) and unsupervised methods (learning from data without a definitive outcome) will be presented and discussed. The particular methods and applications covered are based on student interests, determined at the onset of the semester. The instructor introduces the field during the first few weeks of the semester, with instructor, guest, and student research paper presentations taking place thereafter. Student evaluation is based on paper presentations and a semester-long project.
Students from all areas welcome!
*Please find a copy of the syllabus with the general schedule (schedule at the back) here
*Please find a copy of the syllabus with a specific schedule from a recent offering here