In this class you will learn about a variety of methods for discovering patterns in data. The methods include techniques from statistics, linear algebra, and artificial intelligence. Students will be required to solve written exercises, implement a number of machine learning algorithms and apply them to sets of 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.

You are expected to be familiar with the CS Department policy on cheating and with the CS Department Code of Ethics. This course will adhere to the CSU Academic Integrity Policy as found in the General Catalog and the Student Conduct Code. 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.

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. These will be available on the Schedule page the evening of the on-campus lecture.

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 or experience in linear algebra, statistics, and probability, and programming experience with at least two languages. Consult the instructor to determine if you are sufficiently prepared.

Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Peter Flach, Cambridge 2012.

Your grade for this course will be based only on the assignments, most of which will be written reports. Each written report will require you to implement and run a machine learning algorithm and to write the report on your methods, results and conclusions. You must use python for the implementation and latex to write the report. Each report will be graded for correct implementation and results, interesting and thorough discussion, and good organization, grammar and spelling.

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

• 2% for the proposal
• 10% for the written report for on-campus students, 18% for distance-learning students
• 8% for the presentation by on-campus students

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.