Introduction to Machine Learning

10-601B, Fall 2016
School of Computer Science
Carnegie Mellon University


Course Info

Course description

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.

10-601 is open to all but is recommended for CS Seniors & Juniors, Quantitative Masters students, and non-SCS PhD students.

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.

Grading

The requirements of this course consist of participating in lectures, midterm, final, and 7 problem sets. This is a MS 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:

  • 55% for homeworks (you can drop 1 homework)
  • 20% for midterm
  • 20% for final
  • 5% for class participation

Late homework policy

Homework is worth full credit if submitted before the due date, half credit during the next 48 hours, and zero credit after that. Additionally, you are permitted to drop 1 homework.

Audit Policy

Formal auditing of this course is permitted. Please contact the instructors directly to request permission. We will let you know the auditing requirements then.

Pass/Fail Policy

We allow you take the course as Pass/Fail. What grade is the cutoff for Pass will depend on your program.

Homework Resources and Collaboration Policies

Read this carefully!

(Adapted from Roni Rosenfeld’s 10-601 Spring 2016 Course Policies.)

Collaboration among Students

  • The purpose of student collaboration is to facilitate learning, not to circumvent it. Studying the material in groups is strongly encouraged. It is also allowed to seek help from other students in understanding the material needed to solve a particular homework problem, provided no written notes (including code) are shared, or are taken at that time, and provided learning is facilitated, not circumvented. The actual solution must be done by each student alone.
  • The presence or absence of any form of help or collaboration, whether given or received, must be explicitly stated and disclosed in full by all involved. Specifically, each assignment solution must include answering the following questions:
    1. Did you receive any help whatsoever from anyone in solving this assignment? Yes / No.
      • If you answered ‘yes’, give full details: ____________
      • (e.g. “Jane Doe explained to me what is asked in Question 3.4”)
    2. Did you give any help whatsoever to anyone in solving this assignment? Yes / No.
      • If you answered ‘yes’, give full details: _____________
      • (e.g. “I pointed Joe Smith to section 2.3 since he didn’t know how to proceed with Question 2”)
    3. Did you find or come across code that implements any part of this assignment ? Yes / No. (See below policy on “found code”)
      • If you answered ‘yes’, give full details: _____________
      • (book & page, URL & location within the page, etc.).
  • If you gave help after turning in your own assignment and/or after answering the questions above, you must update your answers before the assignment’s deadline, if necessary by emailing the course staff.
  • Collaboration without full disclosure will be handled severely, in compliance with CMU’s Policy on Cheating and Plagiarism.

Previously Used Assignments

Some of the homework assignments used in this class may have been used in prior versions of this class, or in classes at other institutions, or elsewhere. Solutions to them may be, or may have been, available online, or from other people or sources. It is explicitly forbidden to use any such sources, or to consult people who have solved these problems before. It is explicitly forbidden to search for these problems or their solutions on the internet. You must solve the homework assignments completely on your own. We will be actively monitoring your compliance. Collaboration with other students who are currently taking the class is allowed, but only under the conditions stated above.

Policy Regarding “Found Code”:

You are encouraged to read books and other instructional materials, both online and offline, to help you understand the concepts and algorithms taught in class. These materials may contain example code or pseudo code, which may help you better understand an algorithm or an implementation detail. However, when you implement your own solution to an assignment, you must put all materials aside, and write your code completely on your own, starting “from scratch”. Specifically, you may not use any code you found or came across. If you find or come across code that implements any part of your assignment, you must disclose this fact in your collaboration statement.

Duty to Protect One’s Work

Students are responsible for pro-actively protecting their work from copying and misuse by other students. If a student’s work is copied by another student, the original author is also considered to be at fault and in gross violation of the course policies. It does not matter whether the author allowed the work to be copied or was merely negligent in preventing it from being copied. When overlapping work is submitted by different students, both students will be punished.

To protect future students, do not post your solutions publicly, neither during the course nor afterwards.

Severe Punishment of Violations of Course Policies

All violations (even first one) of course policies will always be reported to the university authorities, will carry severe penalties, usually failure in the course, and can even lead to dismissal from the university.

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.