Introduction to Machine Learning

10-701, Fall 2016
School of Computer Science
Carnegie Mellon University


Course Info

Prerequisites

Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. In addition, recitation sessions will be held to review some basic concepts.

Course description

Machine learning studies the question “how can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you.This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.

Textbooks

Grading

The requirements of this course consist of participating in lectures, midterm, 5 problem sets and a project. This is a PhD level class, and the most important thing for us is that by the end of this class students understand the basic methodologies in machine learning, and be able to use them to solve real problems of modest complexity. The grading breakdown is the following:

  • Problem sets (5 assignments, 35%)
  • Midterm (30%)
  • Final project (35%)

The grading breakdown for the components of the final project is the following:

  • Proposal (10%)
  • Midway Report (20%)
  • Final Report (40%)
  • Presentation (30%)

Homework resources and collaboration policy

Homeworks and exams may contain material that has been covered by papers and webpages. Since this is a graduate class, we expect students to want to learn and not google for answers. You should cite the materials you used.

Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You also must indicate on each homework with whom you collaborated.

The final project may be completed by small teams.

Late homework policy

You will be allowed 2 total late days without penalty for the entire semester. You may be late by 1 day on two different homeworks or late by 2 days on one homework. Weekends and holidays are also counted as late days. Late submissions are automatically considered as using late days.

Once those days are used, you will be penalized according to the following policy:

  • Homework is worth full credit before the deadline.
  • It is worth half credit for the next 24 hours.
  • It is worth zero credit after that.

You must turn in at least of the homeworks, even if for zero credit, in order to pass the course.

Auditing

To satisfy the auditing requirement, you must do one of the following:

  • Submit three homeworks, and receive at least 75% of the points on each one.
  • Do a class project, which must address a topic related to machine learning and must be something that you have started while taking this class (i.e. it can’t be something you did last semester). You will need to submit a project proposal with everyone else, and present a poster with everyone. However, you don’t need to submit a milestone or final paper. You must get at least 80% on the poster presentation part of the project.

If you plan to audit the class, please send the instructors an email saying that you will be auditing and telling us which requirement you plan to fulfill.

Support

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night: * CaPS: 412-268-2922 * Re:solve Crisis Network: 888-796-8226 * If the situation is life threatening, call the police: * On campus: CMU Police: 412-268-2323 * Off campus: 911.

If you have questions about this or your coursework, please let the instructors know.


Note to people outside CMU

Please feel free to reuse any of these course materials that you find of use in your own courses.  We ask that you retain any copyright notices, and include written notice indicating the source of any materials you use.