Course Promises

By the end of the course, you will understand:

• several machine learning frameworks, including supervised learning, unsupervised learning, and hybrid approaches
• various algorithms for the frameworks we explore, including the variation in data representation
• how to choose and apply an appropriate framework and algorithm for a new problem
• practical considerations for data, including data preprocessing, feature engineering, and resource constraints
• the core concept of generalization, and the associated theoretical tools for inspecting both our data and models
• theoretical and empirical evaluation of performance

Student Responsibilities

I have outlined the skills and objects this course promises to provide you. For this promises to be upheld, you will need to commit towards the policies outlined below. To succeed you should:

• Attend class.
  The primary introduction to course material is through class lecture. Additionally, we often do learning exercises during class, which give you immediate experience with the material we are covering. While I am more than happy to help with any material in office hours, priority will be given to students who attend and participate in lecture. Office hours are not to make up for missed lecture.
• Attend lab prepared.
  Lab will introduce new content and be an opportunity to work in teams on paired assignments. Lab attendance is mandatory.
• Participate actively in learning process.
  Showing up is necessary, but not sufficient to success in the course. To fully develop your analytical skills, you are expected to participate in class discussion. This includes asking questions during lecture portions and engaging your peers during short class exercises. Studies show active involvement is the number one determinant of student success.
• Prepare for lecture
  We will utilize clickers in this course combined with pre-reading assignments for each lecture. You are expected to have done pre-reading before each lecture as well as reviewing notes from the prior meeting. If you have not done so, you will be unprepared for the daily concept quizzes that begin lecture as well as subsequent group discussions.
• Start the lab assignments early.
  I realize this one is not always easy to do, but if you can get in the habit of doing this, you will be much better off. As the labs get longer and more difficult, starting early will give you plenty of time to mull over the lab problems even when you aren't actively writing your solution.

Academic integrity

Academic honesty is required in all your work. Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. This includes, but is not limited to, obtaining solutions from students who previously took the course or code that can be found online. You may not share solutions after the due date of the assignment.

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read anyone else's code or let anyone else read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class, code found in the course text book, and code worked on with an assigned partner. In these cases, you should always include detailed comments that indicates on which parts of the assignment you received help, and what your sources were.

Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook: "Because plagiarism is considered to be so serious a transgression, it is the opinion of the faculty that for the first offense, failure in the course and, as appropriate, suspension for a semester or deprivation of the degree in that year is suitable; for a second offense, the penalty should normally be expulsion."

The spirit of this policy applies to all course work, including code, homework solutions (e.g., proofs, analysis, written reports), and exams. Please contact me if you have any questions about what is permissible in this course.