CS 251: Syllabus

Syllabus for Spring 2014

Topics and Readings

Learning Goals

  1. Students understand and can write programs to store and manipulate data and measurements.
  2. Students understand and can implement the fundamental concepts of interactive visualization of data.
  3. Students understand and can implement common data transformations and statistical analysis.
  4. Students understand and can make appropriate use of current machine learning techniques for prediction and knowledge discovery.
  5. Students present methods, algorithms, results, and designs in an organized and competently written manner.
  6. Students gain experience working with real data from disciplines outside computer science.

Recommended Textbook

Witten, Frank, and Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011, 3rd Ed.


Grading

In this course there will be regular opportunities for you to practice what you have learned and to demonstrate your accomplishments. Below, is a short description of each opportunity, along with the percentage of your final course grade it represents.

Labs with Programming Projects45%Weekly, hands-on, supervised learning. You will begin a programming assignment in each lab. It will be due the following Monday night
Quizzes25%Short weekly in-class quizzes (given on Fridays)
Short homework assignments5% Assigned each Wednesday, it will help you prepare for the quiz each Friday.
Participation5%Ask questions, answer questions, join in discussions
Final Exam20%An exciting 3-hour opportunity at the end of the semester to demonstrate your ability to answer questions about course material.

This course covers the analysis and visualization of scientific data. Topics will include data management, basic statistical analysis, data mining techniques, and the fundamental concepts of machine learning. Students will also learn how to visualize data using 2-D and 3-D graphics, focusing on techniques that highlight patterns and relationships. Course projects will use data from active research projects at Colby.


Policies

Attendance and Participation

Nothing is more boring than a course with a professor who drones on and on in a lecture about material that the students don't understand. Although this course does involve lecturing, this course will not be boring. I will not drone and I will do my best to make the material as clear as possible. But for this course to be truly successful, your presence and participation are important. When you have a question, ask it. It is highly probable that one of your classmates has the same question. When I give you an opportunity to share your opinion or your answer, please speak up. I want to hear what you have to say. And, of course, to participate in class you must attend class.

Deadlines

The short homework assignments must be turned in on time. No late short assignments will be accepted because we will refer to their solutions in class. The longer programming assignments must be turned in on time for maximal credit. Late labs (programming assignments) will be accepted, but will be given reduced grades. Programming assignments are graded on a 30 point scale. For each day late, your grade will be reduced by 3 points.

Collaboration

Collaboration on programming assignments is encouraged, but you as an individual are responsible for understanding all the material in the assignment. If you have had a substantative discussion of any programming solution with a classmate, then be sure to cite them in your write-up or comments. If you are unsure of what constitutes "substantative", then ask me or err on the side of caution. You will not be penalized for working together. However, you must not copy answers directly (electronically) from another student. I reserve the right to ask you to verbally explain the reasoning behind any code that you turn in.

Academic Honesty

It is vitally important that you turn in work that is your own. As I said above, you may not copy code from another student. However, there are cases in which you may copy code from the web (we actually recommend you do that for the base Dialog class). In these cases, cite the place you got it from in your comments. Getting solutions for small parts of your programming problem is perfectly reasonable. Your overall design, however, should be yours. We tell you not to copy from friends in the course because they are designing the same (or very similar) software. So, just be honest about where you find code. For one-line solutions (such as the sort you might find on Stack Overflow), use your discretion as to whether or not it is necessary to cite them. I am sure I don't need to talk about dishonesty in a class like this, but I feel compelled to point out that, in addition to the ethical implications of dishonesty, you undermine your ability to learn when you cheat. Please read Colby's Academic Integrity and Honesty Statement.

Late Policy:

The weekly assignments will build upon each other, and each week a solution for the prior week's assignment will be posted so that everyone begins each week with a working code base. Assignments turned in after the solutions have been posted will receive no credit. It is better to hand in a partially working assignment than nothing at all.


Topics

WeekTopicsSuggested Reading
1:
  • Introduction, course concept
  • What is data?
  • GUI development in Python
The Web
2:
  • GUI development in Python
  • Coordinate systems and transformations
  • NumPy: working with matrices
Tkinter and Numpy tutorials
3:
  • 2D and 3D viewing pipeline
  • Interactive camera control
  • Visualizing higher dimensions
Lecture notes
4:
  • Charts and histograms
  • Range selection
  • Data characterization
Lecture notes
5:
  • Basic statistics
  • Data transformations
  • Linear regression
Lecture notes, textbook chapter 7
6:
  • Covariance
  • Principal component analysis [PCA]
Lecture notes
7:
  • Noise and variance
  • Overview of machine learning methods
  • Clustering and Distance metrics
textbook chapters 1-3

Spring Break
8:
  • Clustering and Distance metrics
  • Naive Bayes

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Lecture notes, textbook chapter 4
9:
  • Naive Bayes
  • Evaluating classifiers
Lecture notes, textbook chapter 5
10:
  • 1-R and full decision trees
  • Issues in building trees
Lecture notes, textbook chapters 4, 6
11:
  • Artificial Neural Networks
  • Neural Network Variations
  • SNNS
Lecture notes, textbook chapters 4, 6
12:
  • Linear regression
  • Linear regression variations
Lecture notes, textbook chapters 4, 6
13:
  • TBD
Lecture notes