IS 733 Data Mining (Spring 2018)
Information Systems Department
University of Maryland Baltimore County
Baltimore, Maryland 21250
Departmental Office: Room ITE 404, ph. 410-455-3206
This course will provide an in-depth understanding of the technical, business, and research issues in the area of data mining, including classification, clustering, Bayesian networks, association rules, visualization, and a brief introduction to data warehousing. New areas of research and development in data mining will also be discussed.
Student learning outcomes: By the end of this course, you will be able to:
- Apply a variety of data mining techniques to real-world situations,
- select appropriate strategies for each step in the data mining process, and
- discuss the underlying theoretical principles behind data mining methods, and the practical implications of these.
Lecture time and venue: Wed 4:30pm - 7:00pm Sherman Hall 014
Instructor: Dr. James Foulds
Instructor email: jfoulds [at] umbc [dot] edu. Please use Piazza for course-related questions, instead of email, so that everyone can benefit from the answers.
Instructor office hours: Wed 3 - 4pm ITE 447 (other times by appointment)
Piazza: Sign up for this course at piazza.com/umbc/spring2018/is733
Poll Everywhere: Vote on in-class poll questions at PollEv.com/jamesfoulds656 . Register your account for the course at polleverywhere.com/register?p=6bu49-6kgn&u=wJdvD6sW , by week 2 in order to get participation credits.
- Requires IS 620, or consent of the instructor.
- Required knowledge: basic programming ability in a high-level language such as Java, Python, R, or Matlab. No previous background in data mining is required. Although the course will be relatively non-technical, a basic understanding of elementary concepts in continuous and discrete mathematics will be needed (linear algebra, Boolean logic, graphs and trees, ...).
Required TextbookData Mining: Practical Machine Learning Tools and Techniques, Fourth Edition (Witten et al.) is the primary textbook. You will need this book for course readings. Until you obtain it, the UMBC library has an electronic copy for online access that you can use. Earlier editions of the textbook are acceptable but not recommended. Some material will be missing, and it will be up to you to convert chapter/section/page numbers to the older edition for the required readings.
Course Requirements and Grading
- Homeworks 25% (5 of them, 6.25% each for your best 4 homeworks; the lowest score will be dropped)
- Group projects 35%:
- Proposal 5% (due 2/28/2018)
- Mid-term report 5% (due 4/4/2018)
- Group project poster 10% (presented in class 5/9/2018, digital copy due at the same time)
- Final report 15% (due 5/11/2018 )
- Final 35%
- Participation 5%
The project will be done in groups of 4-5. Project proposals are to be sent to me by email, and approved by the deadline.
In this course, participation means more than just showing up. It also refers to contributing to everyone's learning, through active engagement in peer instruction exercises, in-class discussions, and Piazza questions/answers. Participation grades will be assessed as a percentage of peer instruction questions answered (correctly or not), with a 90% response rate being sufficient for full points. Contributions on Piazza will be awarded bonus participation points, not to exceed the maximum participation grade otherwise attainable.
With respect to final letter grades, UMBC's Catalog states that an A indicates "superior" achievement; B, "good" performance; C, "not satisfactory"; D, "unacceptable"; F, "failure." There is specifically no mention of any numerical scores associated with these letter grades. Below is how grades may be assigned based on your final points, accumulated over the semester. Grades will be assigned using a plus/minus system. It is university policy that A+, D+, and D- are not assigned. I do not grade on a curve, so that everyone in the class has the opportunity to succeed.
|Final Grade||Letter Grade||Points when calculating GPA|
|91 - 100||A||4.0|
|89 - 90.99||A-||3.7|
|87 - 88.99||B+||3.3|
|81 – 86.99||B||3.0|
|79 - 80.99||B-||2.7|
|77 – 78.99||C+||2.3|
|71 – 76.99||C||2.0|
|69 - 70.99||C-||1.7|
|60 – 68.99||D||1.0|
|0 – 59.99||F||0.0|
Homework and Exam Policies
- Homeworks are due at the beginning of class on the dates specified. Late homeworks will not be accepted unless an extension is approved by me in advance. Requests for extensions must be made at least three days before the due date.
- In the event of class cancellation due to inclement weather, any hard-copy paper assignment or test will be due in the next class meeting. Electronic submissions will still be due on the original due date.
|1/31/2018||Week 1||Course overview, introduction to data mining||Applications, the data mining process, data mining ethics||Witten et al., Ch 1.|
|2/7/2018||Week 2||Know your data||Instances and attributes, plotting and visualization||HW1 out||Witten et al., Ch 2.|
|2/14/2018||Week 3||Data preprocessing||Data cleaning, integration, transformation, reduction, discretization. Principal components analysis.||Kotsiantis, S. B., D. Kanellopoulos, and P. E. Pintelas. "Data preprocessing for supervised learning." International Journal of Computer Science 1.2 (2006): 111-117|
|2/21/2018||Week 4||Data Warehousing||OLAP vs OLTP, data cubes. Project brainstorming.||HW1 due, HW2 out. Project groups formed by this date||Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1), 65-74, up to Section 4|
|2/28/2018||Week 5||Knowledge representation||Linear models, trees, rules. Sharing project ideas||Project proposal due||Witten et al., Ch 3|
|3/7/2018||Week 6||Supervised learning||Naive Bayes, decision trees, nearest neighbors||HW2 due, HW3 out||Witten et al., Ch 4.1 - 4.4|
|3/14/2018||Week 7||Supervised learning (continued)||Logistic regression, support vector machines, evaluating classifiers.||Witten et al., Ch 4.6, 5 (up to 5.3), 7.2|
|3/21/2018||Week 8||Spring Break|
|3/28/2018||Week 9||Ensemble methods||Bagging, boosting, random forests, stacking||HW3 due, HW4 out||Witten et al., Ch 12|
|4/4/2018||Week 10||Unsupervised learning||Association rule learning, K-means, hierarchical clustering, mixtures of Gaussians||Project mid-term progress report due||Witten et al., Ch 4.5, 4.8, 9.3|
|4/11/2018||Week 11||Guest lecture, Dr. Shimei Pan||Social media analytics||HW4 due, HW5 out|
|4/18/2018||Week 12||Recommender systems||Content filtering, collaborative filtering, hybrid recommender systems||Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8)|
|4/25/2018||Week 13||Text mining||Bag of words representation, n-grams, latent semantic analysis, topic models, word embeddings||HW5 due||Witten et al. Ch 13.5|
|5/2/2018||Week 14||Deep learning||Deep feedforward networks, backpropagation, convolutional neural nets||Witten et al., Ch 10.1 - 10.3, 10.6|
|5/9/2018||Week 15||Group project presentations||Digital copies of posters due. Project final report due 5/11/2018 (11:59pm)|
|5/16/2018||Study day||No class|
|5/21/2018 (Monday)||Exam week||Final exam 3:30-5:30pm (Sherman Hall 014)|
The schedule may be subject to change. The summary and details columns are only a guideline of the content likely to be covered, and the dates on which material is covered may shift.
Traditional lectures will be augmented with active learning methods, primarily in the form of peer instruction exercises. Research has strongly indicated that active learning improves student outcomes in STEM fields versus traditional lecturing (Freeman et al., 2013). We will be using the Poll Everywhere service for polls and quizzes. You will need to bring a mobile device, laptop, or tablet to class in order to participate in the exercises. If you do not have a suitable device, please let me know as soon as possible.
Pre-class reading assignments will be given for each lesson, which are very important for learning, and for making the best use of our limited time together (a partially "flipped classroom" approach). These readings are therefore required.
SoftwareThis course will make extensive use of the free, open source WEKA data mining toolkit.
UMBC's policies on academic integrity will be strictly enforced (see the University System of Maryland's policy document, UMBC's academic integrity overview page, the student academic conduct policy and the UMBC catalog). In particular, all of your work must be your own. Acknowledge and cite source material in your papers or assignments. While you may verbally discuss assignments with your peers, you may not copy or look at anyone else's written assignment work or code, or share your own solutions. Any exceptions will result in a zero on the assessment in question, and may lead to further disciplinary action. Some relevant excerpts from UMBC's policies, as linked to above, are:
- "By enrolling in this course, each student assumes the responsibilities of an active participant in UMBC's scholarly community in which everyone's academic work and behavior are held to the highest standards of honesty. Cheating, fabrication, plagiarism, and helping others to commit these acts are all forms of academic dishonesty, and they are wrong." (UMBC's academic integrity overview)
- "Students shall not submit as their own work any work which has been prepared by others." (USM policy document)
- "Students shall refrain from acts of cheating and plagiarism or other acts of academic dishonesty." (USM policy document)
- "Plagiarism means knowingly, or by carelessness or negligence, representing as one's own, in any academic exercise, the intellectual or creative work of someone else." (student academic conduct policy)
- "Cheating means using or attempting to use unauthorized material, information, study aids, or another person’s work in any academic exercise" (student academic conduct policy)
Accessibility in the Classroom; Student Support / Disability Services
UMBC is committed to eliminating discriminatory obstacles that may disadvantage students based on disability. Student Support Services (SSS) is the UMBC department designated to:
- receive and maintain confidential files of disability-related documentation,
- certify eligibility for services,
- determine reasonable accommodations,
- develop with each student plans for the provision of such accommodations, and
- serve as a liaison between faculty members and students regarding disability-related issues.
Diminished mental health can interfere with optimal academic performance. The source of symptoms might be related to your course work; if so, please speak with me. However, problems with other parts of your life can also contribute to decreased academic performance. UMBC provides cost-free and confidential mental health services through the Counseling Center to help you manage personal challenges that threaten your personal or academic well-being.
Remember, getting help is a smart and courageous thing to do -- for yourself and for those who care about you. For more resources get the Just in Case mental health resources Mobile and Web App. This app can be accessed by clicking: counseling.umbc.edu/justincase.
The UMBC Counseling Center is in the Student Development & Success Center (between Chesapeake and Susquehanna Halls). Phone: 410-455-2472. Hours: Monday-Friday 8:30am-5:00pm.
Diversity Statement on RespectStudents in this class are encouraged to speak up and participate during our meetings. Because the class will represent a diversity of individual beliefs, backgrounds, and experiences, every member of this class must show respect for every other member of this class. (Statement from California State University, Chico’s Office of Diversity and Inclusion).
Family Educational Rights and Privacy Act (FERPA) Notice
Please note that as per federal law I am unable to discuss grades over email. If you wish to discuss grades, please come to my office hours.
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- The Mosaic Center for Culture and Diversity: osl.umbc.edu/diversity/mosaic
- Career Center's resources for diverse populations (including student organizations): http://careers.umbc.edu/students/resources/diverse/
- Resources for LGBTQ students: osl.umbc.edu/lgbtq/community_resources/
- Office of International Education Services (IES): ies.umbc.edu/
- Information regarding recent executive actions: ies.umbc.edu/executive-actions/
- Wellness Initiative: wellness.umbc.edu/
- Counseling Center: counseling.umbc.edu/
- Women's Center: womenscenter.umbc.edu/
- Center for Women in Technology (CWIT): cwit.umbc.edu/
- Women Involved in Learning and Leadership (WILL) Program: gwst.umbc.edu/will/
- Sexual assault and relationship violence on-campus resources: womenscenter.umbc.edu/sexual-assault-and-relationship-violence-response-team-and-umbcs-voices-against-violence/
- Sexual misconduct policies and procedures (including filing a complaint): humanrelations.umbc.edu/sexual-misconduct/policies-and-procedures/
- University System of Maryland’s Policy of Non-Discrimination on the Basis of Sexual Orientation and Gender Identity or Expression: humanrelations.umbc.edu/files/2014/12/USMPolicyNonDiscrimSOrientGenderIEJune2012.pdf
- Office of Student Disability Services: sds.umbc.edu/
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- Graduate Student Association: gsa.umbc.edu/
- Graduate Student Association Writing Advisor: gsa.umbc.edu/writing-advisor/