Course Description

A survey of web data mining techniques. Students will learn to program in Python and learn the mathematical techniques for mining the web and interacting with web APIs from popular web sites for data collection. The course is designed in two parts, the first part will cover Python programming language and how to build applications, the second part is more into mining the web by covering topics that will include recommendation systems, clustering, ranking, optimization, classifiers, decision trees, k-nearest neighbors, kernel methods and support vector machines, feature extraction and genetic programming. The grade will be based on completing assignments from the text book and class participation. The students will learn when and how to apply the various web mining techniques in real applications. Throughout the semester and on a weekly basis there will be extra challenges which will be rewarded with extra credit upon successful completion.

 

Course Overview

• In this course you learn how to program in python from novice to expert.
• You will learn how the web works, how search engines function. You will learn the mining techniques of the web from recommendation systems, clustering, ranking, optimization, classifiers, decision trees, k-nearest neighbors, kernel methods and support vector machines, feature extraction and genetic programming.
• This hands-on course and projects will enable you to apply the python programming skills you learned along with web mining techniques to build real useful applications.
• Extra credit will be awarded on peer improvement as described in the slides of lecture 1.
• Students will work individually on weekly assignments and will be required to present their work on a weekly basis through a speed demo.
• There will be no exams in this course, marks will be awarded for weekly assignments, speed demos, peer-improvement extra credit opportunities, and class participation.
• Students are encouraged to bring their laptops to class during the code walkthroughs sections.

 

Prerequisites

No specific course prerequisite. However, students must be comfortable with programming and coding in general. In addition, students are encouraged to bring their laptops to class during the code walkthroughs sections.

 

CRN Identifier

The CRN identifier for registeration is: CRN 31383.

 

Text

The required text will be:

Programming Collective Intelligence: Building Smart Web 2.0 Applications By Toby Segaran [$26.39 at Amazon].

Recommended but not required purchases: 

Python in a Nutshell, 2nd Edition By Alex Martelli [$24.08 at Amazon]

 

Grading Scheme

The class grade will consist of 11 assignments of 10 points each. The instructor will pick the highest 10 marks for the assignments and count them towards the final total of 100 points. The points of each assignment will be split into two portions, 8 points for the assignment submission and 2 for providing a 5 minute demo to the class weekly. Failing to present this demo will result in losing the 2 points unless prior arranged with the instructor.

Every week, after each student presented their work to the class, the students assignments will be opened for claiming. Each student can pick a colleague's assignment and offer an improvement to their work as explained in the slides of lecture 1. A student cannot claim their own work. The following week each student, who claimed a colleague's assignment to improve, will get 3 minutes to describe their improvement for a chance of being awarded 0-2 extra points by the instructor. Each student whose assignment have been picked to be improved will be awarded 1 bonus point for each non 0-points improvements by colleagues. Students should claim the assignments to improve by emailing the mailing list and the claiming is done in a first come first serve manner.

 

Academic Integrity / Honor Code

By attending Old Dominion University you have accepted the responsibility to abide by the honor code. If you are uncertain about how the honor code applies to any course activity, you should request clarification from the instructor. The honor code is as follows:

"I pledge to support the honor system of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member if the academic community, it is my responsibility to turn in all suspected violators of the honor system. I will report to Honor Council hearings if summoned."

In particular, submitting anything that is not your own work without proper attribution (giving credit to the original author) is plagiarism and is considered to be an honor code violation. It is not acceptable to copy source code or written work from any other source (including other students), unless explicitly allowed in the assignment statement. In cases where using resources such as the Internet is allowed, proper attribution must be given.

Any evidence of an honor code violation (cheating) will result in a 0 grade for the assignment/exam, and the incident will be submitted to the Department of Computer Science for further review. Note that honor code violations can result in a permanent notation being placed on the student's transcript. Evidence of cheating may include a student being unable to satisfactorily answer questions asked by the instructor about a submitted solution. Cheating includes not only receiving unauthorized assistance, but also giving unauthorized assistance. For class files kept in Unix space, students are expected to use Unix file permission protections (chmod) to keep other students from accessing the files. Failure to adequately protect files may result in a student being held responsible for giving unauthorized assistance, even if not directly aware of it.

Students may still provide legitimate assistance to one another. You are encouraged to form study groups to discuss course topics. Students should avoid discussions of solutions to ongoing assignments and should not, under any circumstances, show or share code solutions for an ongoing assignment.

Please see the ODU Honor Council’s webpage for other concrete examples of what constitutes cheating, plagiarism, and unauthorized collaboration. All students are responsible for knowing the rules. If you are unclear about whether a certain activity is allowed or not, please contact the instructor.

 

Course Policies

Grading

The grading scale is as follows:

Late Assignments

Late assignments are accepted with a 3 points deduction for every 24 hours passing. 24 hours and one minute will result in deducting another 3 points of the assignment mark. The deduction will reach the limit when all the points assigned to this assignment are deducted.

Attendance

Attendance is required for class participation, presentation, and discussion. I expect you to attend class and to arrive on time. Your grade may be affected if you are consistently tardy. If you have to miss a class, you are responsible checking the course website to find any assignments or notes you may have missed. Students may leave after 15 minutes if the instructor or a guest lecturer does not arrive in that time. Absences must cleared with the instructor prior to class. You are responsible for everything that is said, discussed, and presented during class.

Email

Students should check the course group and emails every day, the students are responsible for all the details and updates posted on this group and mailing list.

Classroom Conduct

Please be respectful of your classmates and instructor by minimizing distractions during class. Cell phones must be turned off during class.

Class Mailing List

Students should join this group/mailing list: CS495-fall12