DSC 540 Advanced Data Mining
The course is for students with prior background in data mining or machine learning techniques. The course will cover advanced modeling techniques, such as ensemble learning, extended linear models, probabilistic graphical models, bayesian inference and kernel methods. 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 data mining techniques. Applications of the models will be presented in various domains, including social computing, market research, visual computing, and biomedical and health informatics.
• Introductions, syllabus and tentative schedule
• Overview of Machine Learning and AI
• Applications of Machine Learning
o Bias and Variance, Regularization
• Performance evaluation
o Classification performance metrics
• Resampling Methods
o Bootstrap, Cross-validation
• Ensemble Methods
o Bagging, Random Forests
• Ensemble Methods cont.
o Boosting, Stacking
• Project review and discussion
• Support Vector Machines (SVM)
o Linear SVM
o Kernel SVM, RBF
• Project review and discussion cont.
• Bayesian Inference
o Bayesian Data Analysis
o Priors, Maximum a Posteriori (MAP)
o Bayesian Regression
• Probabilistic Graphical Models
o Bayes Nets Representation
o Bayes Nets Inference
• Probabilistic Graphical Models cont.
o Bayes Nets Learning
o Markov Models
o Hidden Markov Models
• Reinforcement Learning
o Markov Decision Processes (MDP)
o Value Functions & Bellman Equations
• Recommendation Systems
o Collaborative Filtering
o Market Basket Analysis
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