Course Syllabus

• Course Details
• Course description
• Grading
• Exams
• Reviews
• Project
• Honor policies
• Course Schedule
• Assignment web page

Course Details

Course Description

Students will be provided survey of contemporary and historical representations and techniques used in the field of Machine Learning. They will be required to develop knowledge of, and analytical skills regarding, a variety of supervised, unsupervised, and reinforcement learning techniques. Students will be expected to understand these techniques, as well as many of the research issues they elicit and the applications for which they may be employed.

The class has two high-level goals. First, it seeks to introduce students to a wide variety of machine learning techniques in order that they might have the opportunity to begin building their knowledge of the types of tools available to them. Second, it seeks to help students develop the empirical and analytical skills needed to contribute to the study of this field.

I expect students to demonstrate their knowledge and skills in both graded and ungraded components of the course. That is, class participation in the form of questions and discussion are vital parts of a graduate student's eductation.

Grading

We will attempt to follow the course schedule as closely as possible. Student grades will be assessed from two exams, several paper reviews, and one project. Their relative weights are as follows:

• Exams: 50%
• Reviews: 20%
• Project: 30%

The higher of the two exam scores will count 30%, the lower will count 20%.

Additionally, homework will be assigned periodically during the semester. Students are expected to complete the homework, but it will not be considered part of the grade. Consider the homework part of good exam preparation.

Reviews

Students will be assigned papers to review throughout the semester. You will be given two weeks to review a paper, once assigned, and you must work independently. Specifics on how to write these reviews will be provided with the first such assignment. In general, consider the assignments to be realistic, journal-level reviews. Students will be expected to assess the contribution level, the methodological and technical soundness, and the readability of the articles in question; you will have to make an assessment as to whether or not the paper should have been published and to defend that assessment.

Reviews will be submitted via turnitin.com.

Project

Students will be expected to complete a final research project. You may partner in groups of size two (or three, under extreme circumstances). A conference-style paper must be submitted, and a short oral presentation will be required. The research must be original and the contributions should be made clear. You are encouraged to seek publication for this project after or during the class.

Submission of project proposals will be required by a date I will specify soon. This proposal will describe the student team members, the proposed research, and a brief description of your approach. If you do not submit a proposal on time, I'll pair you up myself and assign a project (you don't want that).

The paper portion of the project will be submitted via turnitin.com. Late projects will not be accepted.

Honor policies

Students are referred to UCF's honor policy for review. In addition, students may find the following information useful:

• Writing Resources from UCF's Writing Center
• A definition for plagiarism here

Honor violations will result in at minimum a zero on whatever is being graded (exam, project, review, etc.), a drop in a letter grade, and notification of incident to UCF office of Student Conduct. I will not tolerate ANY cheating. I will not be lenient, so do not ask.

The best (and the smartest) way to avoid any trouble is simply not to cheat. There are no "gray areas" with respect to cheating. If you are concerned that some choice you may make could be construed as unethical, you are free to consult me. I will clarify any sticky areas.

Additionally, please take note of the following disclaimer:

   As graders, we reserve the right to compare content submited
   by students to alternative content (either students' or
   otherwise) by any means necessary (automated or non-automated).