User Tools

Site Tools


syllabus

Making the simple complicated is commonplace; making the complicated simple, awesomely simple, that's creativity. – Charles Mingus

Description

In this class you will learn about a variety of methods for discovering patterns in data. Students will be required to solve written exercises, implement and use machine learning algorithms and apply them to data, and hand in written reports describing the results.

Coding will be in Python and written reports will be prepared with LaTeX; both are freely available on all platforms.

A lot of material will be covered in this course. Students are expected to speak up in class with questions and observations they have about the material. Do not expect to be able to complete all assignments working on your own and without asking any questions. If you find yourself wondering what the next step is in finishing an assignment, please feel free to e-mail the instructor. You may also discuss assignments with other students, but your code and report must be your own work.

This semester we will be using Piazza for class discussions. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and your instructor. Rather than emailing questions to the teaching staff, you are encouraged to post your questions on Piazza.

You are expected to be familiar with the CS Department policy on cheating and with the CS Department code of conduct. This course will adhere to the CSU Academic Integrity Policy. At a minimum, violations will result in a grading penalty in this course and a report to the Office of Conflict Resolution and Student Conduct Services.

Time and Place

Lectures: Tuesdays and Thursdays, 3:30pm - 4:45pm in Behavioral Sciences 103.

On-campus and distance-learning students will be able to watch video recordings of lectures.

Prerequisites

CS440, Introduction to Artificial Intelligence, is a prerequisite for this course. Discuss this with the instructor if you have not taken CS440. Most helpful for CS545 are courses in linear algebra, statistics, and probability, and programming experience with at least two languages. Consult the instructor to determine if you are sufficiently prepared.

Textbook

Learning from Data. Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin, 2012. Be careful when buying this book on Amazon - it should cost you around $28!

Course Staff

Office Office Hours
Instructor: Asa Ben-Hur CSB 448 Mon 10-11am, Thursday 1-2pm
GTA: Tomojit Ghosh tomojit@rams.colostate.edu Tue 6-8pm, Wed 6-8pm (in CSB 120)

There are established benefits of visiting us in our office hrs!

Grading

The majority of the grade will be based on assignments (95%), which will be submitted as code and written reports. You must use python for the implementation and latex to write the report. Each report will be graded for correct implementation/results, a thorough discussion of the results, and good organization, grammar and spelling.

There will be 5-6 regular assignments during the semester, and a final assignment, which is a project designed by you and is worth 20% of the grade. This 20% will be composed of

  • A proposal describing what you will do (2%)
  • A written report describing what you have done (13%)
  • Poster presentation for the on-campus students (5%)
  • A video presentation for the online students (5%)

The calculation of the final letter grade will be made as follows:

  • A 90 - 100%
  • B 80 - 89.9%
  • C 70 - 79.9%
  • D 60 - 69.9%
  • F below 60%

These ranges for a letter grade might be shifted a little lower, but will not be raised.

Late reports will not be accepted, unless you make arrangements with the instructor at least two days before the due date.

syllabus.txt · Last modified: 2016/09/12 18:55 by asa