DSC 345 Machine Learning
The course is for students with prior background in data mining or machine learning techniques. The course will cover advanced modeling techniques, including ensemble learning, extended linear models and kernel methods (PCA, support vector machines), probabilistic graphical models, bayesian networks, mixture and latent variable models, biologically-inspired computing, feature selection and feature engineering techniques, Markov models, and temporal modeling to find patterns over time. First the theoretical foundations of these techniques will be presented and augmented with in-class examples and homework problems. Second, the state-of-the-art research related to these techniques will be presented and augmented with paper reviews that highlight the practical applications of these advanced machine learning techniques. Applications of the models will be presented in various domains, including social computing, visual computing, and biomedical and health informatics.
Course Learning Goals:
- understand the basics behind each machine learning method, as well as the respective cons and pros, for solving data science problems
- understand how to implement machine learning models on real world data using tools such as Python, Scikit-learn, and Spark
- understand methods to evaluate the performance of machine learning models
- understand how information in real world applications can be formulated as different data structures, and the effects of feature selection and other preprocessing techniques
- select, combine, and apply specific machine learning techniques for certain data types and challenges, and understand, explain, and interpret the obtained results
- identify recent trends and open directions in the field of machine learning
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course
content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The
evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely
separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation.
Students complete the evaluation online in CampusConnect.
This course will be subject to the university's academic integrity policy. More information can be found at http://academicintegrity.depaul.edu/ If you
have any questions be sure to consult with your professor.
All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.
Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that
you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002