Lectures: Tuesday & Thursday 4:50pm-6:05pm, Dewey 1101
Web: BlackBoard and https://learn.rochester.edu
Course Teaching Assistants
- Graduate TA : Jingyang Lin jlin81@UR.Rochester.edu
- Puhua Ye pye3@u.rochester.edu
- Maria Rojas Tawil mrojasta@u.rochester.edu
- Bryce Berkhof bberkhof@u.rochester.edu
- Ajay Patel apatel79@u.rochester.edu
Course Description
This course is designed to develop your understanding of the fundamental concepts in data mining and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will help you understand the mindset of creating a data project pipeline by covering topics on model development, causal inference, and traditional data mining. The class will both emphasize individual work and also team work by helping students solidify their knowledge through a final project.
Course Goals
- Apply advanced data analytics methods to solve real world problems
- Generate predictions for classification and regression problems using cutting edge machine learning techniques
- Evaluate the performance of predictions and choose between potential models
- Develop strategies to detect outliers in your dataset
- Handle missing data with both simple and sophisticated techniques
- Describe structures in data using unsupervised algorithms
- Reduce dimensionality of data for easier storage, computation, and analysis
- Understand and implement analytics algorithms from primary sources and documentation
- Estimate algorithm run times and efficiently handle large computation
Details are available in the Course Calendar.
Prerequisites
CSC171, CSC 172 and MTH 161. Some knowledge of probability theory. Recommended: CSC 242 or CSC262; MTH165.
Textbook
Data Mining Concepts and Techniques 3rd Edition Jiawei Hand, Hicheline Kamber, Jian Pei
Course Work and Grading
Final course grades will be based on the following components and weights:
| Weekly on line concept quizzes | 30% |
| Homework Assignments (4 Labs 10% each) | 40% |
| Project Proposal | 5% |
| Project Written Report | 15% |
| Project Presentation | 10% |
Late homework or projects without suitable prearranged excuse will not be accepted. Missed or late homework will receive a grade of zero.
Homework and Projects are due at 11:55pm on the due date. All submission on Blackboard online. Only the latest submission is considered.Programming will be required for the final project. Use of online resources is allowed as long as proper attribution is given.
Regrading deadline is one week from the date grades are posted.
Final Project
The goal of the project is to develop a data mining solution to predict an outcome, discover patterns or classify events.
Team-based activity (maximum team size is 2). Each team will choose from one of the suggested data sets.
Expectation:
- Demonstrate solid understanding of the date (e.g. User descriptive statistics, visualizations, descriptions and features)
- One or more of (in increasing order of complexity and hence merit) :
- Apply a simple baseline algorithm covered in class
- Perform a comparative study using different existing algorithms
- Apply a modification to a known algorithm
- Develop and apply a new algorithm
Letter Grades
Letter grades will follow the Official University of Rochester Grading Scheme. Note that the University scheme puts average
somewhere between C and B. The following table is an estimate of how the numeric grades will map onto the letter grades (subject to change):
| A: Excellent | >=90% |
| B: Above Average | >=80% |
| C: Minimum Satisfactory Grade | >=70% |
| D: Minimum Passing Grade | >=60% |
| E: Fail | <60% |
All appeals of grades for any component of the course (homework, project, quiz, exam, etc.) must be made within ONE WEEK of the grade being posted.
Course Policies
We hope that you will want to attend class (online lecture), but attendance is NOT required. If you chose not to attend, you may miss important announcements or information about the course. All lectures will be recorded.
Computer, crashes, malfunctions, and catastrophic loss of files is NOT an excuse. Backup your files regularly to at least one external drive and/or cloud storage.
Students with an appropriate excuse for missing a quiz, workshop, homework, or project deadline must make arrangements in advance.
Students with an accommodation for any aspect of the course must make arrangements through the Center for Excellence in Teaching and Learning (CETL) in advance. Then, as instructed by CETL, contact the instructor to confirm your arrangements. Do not leave this until the last minute.
Students who are unable to attend or complete any part of the course due to illness should contact the instructor AS SOON AS POSSIBLE. Please note that the University Health Service (UHS) does not provide retroactive excuses for missed classes. Students who are seen at UHS for an illness or injury can ask for documentation that verifies the date of their visit(s) to UHS without mention of the reason for the visit. Students with extended or severe illness should contact the College Center for Advising Services (CCAS) for advice and assistance.
Academic Honesty and Collaboration
All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available at: www.rochester.edu/college/honesty
Your TAs and are available to help you with homework. You may verbally discuss assignments with tutors and other students.
Collaboration on projects is permitted, subject to the following requirements:
- Homework/Lab groups of no more than 2 students
- Project groups of no more than 4 students
- One member of the group should submit on the group's behalf and the grade will be shared with other members of the group. Other group members should submit a short comment naming the other collaborators.
- All members of a collaborative group will get the same grade on the project.
Policy on Electronics
You do not need any electronics in class and they will not help you or your fellow students seated near you.
Unless you are taking notes with a laptop, you do not need it.
Even if you think you want to take notes with a laptop, you may be interested to know that research shows that students who take notes using pen and paper retain significantly more of the information. Typing your handwritten notes into the computer after class improves understanding even more.
Please note Section V.7 of the College's Academic Honesty policy regarding Unauthorized Recording, Distribution or Publication of Course-Related Materials.
CETL Tutoring
Tutoring is a free service available for any undergraduate of the University of Rochester. The Center for Excellence in Teaching and Learning (CETL) provides structured tutoring services.
CETL Tutoring : www.rochester.edu/college/cetl/undergraduate/tutoring.html
CSUG Tutoring
CSUG is the Computer Science Undergraduate Council. These students graciously volunteer their time to help other students, especially students in introductory and core courses.
CSUG Tutoring Schedule: www.csug.rochester.edu/ugc/tutoring/
If you visit the CSUG tutors, please be respectful of their time and COME PREPARED. Try to solve the problem/question BEFORE going to CSUG Tutoring. Go to lab and work on the problem with the TAs BEFORE going to CSUG Tutoring. Go to workshop and discuss the problem with your colleagues BEFORE going to CSUG Tutoring. You MUST be able to show them what you have tried BEFORE you came to them.