Class Calender
| Week |
Date |
Topics & Reading
Assignments |
Presenter/Slides |
|
1 |
Wed Aug 29
|
- Introduction and Administrivia
- Introduction to Python |
slides |
|
2 |
Wed Sep 05
|
- Python in Depth |
slides |
|
3 |
Wed Sep 12
Fri Sep 14
|
- Introduction to Collective Intelligence & Machine Learning (Chapter 1)
- Recommendation Systems (Chapter 2)
- Submission of Assignment 1
- Assignment 1 Demos
- Open codes for peer-improvement phase.
- Open peer-improvement submission. |
slides
Code |
|
4 |
Wed Sep 19
Fri Sep 21
|
- Clustering (Chapter 3)
- Submission of Assignment 2
- Assignment 2 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides
Code |
|
6 |
Wed Sep 26
|
- No Class: Instructor at TPDL2012
- Submission of Assignment 4 |
|
|
5 |
Wed Oct 03
Fri Oct 05
|
- Document Filtering (Chapter 6)
- Submission of Assignment 3
- Assignment 3 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides
Code |
|
7 |
Wed Oct 10
Fri Oct 12
|
- Crawling, Searching, and Ranking (Chapter 4)
- Assignment 4 Demos
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides
Code |
|
8 |
Wed Oct 17
Fri Oct 19
|
- Optimization (Chapter 5)
- Submission of Assignment 5
- Assignment 5 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides
Code |
|
9 |
Wed Oct 24
Fri Oct 26
|
- Decision trees (Chapter 7)
- Submission of Assignment 6
- Assignment 6 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides |
|
10 |
Wed Oct 31
Fri Nov 02
|
- K-Nearest Neighbors (Chapter 8)
- Submission of Assignment 7
- Assignment 7 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides |
|
11 |
Wed Nov 07
Fri Nov 09
|
- Advanced Classification: Kernel Methods and SVMs (Chapter 9)
- Submission of Assignment 8
- Assignment 8 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides |
|
12 |
Wed Nov 14
Fri Nov 16
|
- Feature Extraction (Chapter 10)
- Submission of Assignment 9
- Assignment 9 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides |
|
13 |
Wed Nov 21
|
- No Class...Turkey Week!
- Submission of Assignment 10 |
|
|
14 |
Wed Nov 28
Fri Nov 30
|
- Evolving Intelligence: Genetic Programming (Chapter 11)
- Assignment 10 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
slides |
|
15 |
Wed Dec 05
Fri Dec 07
|
- Submission of Assignment 11
- Assignment 11 Demos
- Peer-Improvement Presentations
- Open codes for peer-improvement phase.
- Open peer-improvement submission.
|
|
|
16 |
Wed Dec 12 |
- No Class: Exams Week |
|
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, Assignment Demos, peer-improvement extra credit opportunities, and class participation.
- Students are encouraged to bring their laptops to class during the code walkthroughs sections.
CRN Identifier
The CRN identifier for registeration is:
CRN 31383.
Syllabus
You can find a detailed version of the syllabus here: CS495-Syllabus
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:
Class Mailing List
Students should join this group/mailing list: CS495-fall12
