10-606/607, Fall 2018
School of Computer Science
Carnegie Mellon University
This page contains the syllabus for both 10-606 and 10-607, since their policies are nearly identical. They differ in course content, and any differences are highlighted below. Since many students take both, we present information about the two together.
10-606:
This course provides a place for students to practice the necessary mathematical background for further study in machine learning. Topics covered include probability (random variables, modeling with continuous and discrete distributions), linear algebra (inner product spaces, linear operators), and multivariate differential calculus (partial derivatives, matrix differentials). The course assumes some background in each of the above, but will review and give practice in each. (It does not provide from-scratch coverage of all of the above, which would be impossible in a course of this length.) Some coding will be required: the course will provide practice with translating the above mathematical concepts into concrete programs.
In order to study the relevance of these topics to machine learning we will apply these concepts to several applications including:
10-607:
This course provides a place for students to practice the necessary computational background for further study in machine learning. Topics covered include computational complexity, analysis of algorithms, proof techniques, optimization, dynamic programming, recursion, and data structures. The course assumes some background in each of the above, but will review and give practice in each. (It does not provide from-scratch coverage of all of the above, which would be impossible in a course of this length.) Some coding will be required: the course will provide practice with translating the above computational concepts into concrete programs.
In order to study the relevance of these topics to machine learning we will apply these concepts to several applications including:
Relation Between 606 and 607:
These two minis are intended to prepare students for further study in machine learning – particularly for taking 10-601 and 10-701. One of the courses (10-606) focuses on mathematical background, and the other course (10-607) focuses on computational background. Most students take both mini courses, but this is not required. 10-606 is not a prerequisite of 10-607.
For more details about topics covered, see the Schedule page.
10-606:
Below is a summary of the background required for this course:
10-607:
There are no required textbooks for this course. However, the following textbooks are recommended as a way to get an alternate presentation of some of the course material:
The requirements of this course consist of participating in lectures, final exam, homework assignments, and readings.
The grading breakdown is the following:
Grade cutoffs:
Each individual component (e.g. an exam) or the overall grades may be curved upwards at the end.
You are required to attend the final exam. For 10-606, the final exam occurs at the end of Mini-I (i.e. mid-semester). For 10-607, the final exam occurs at the end of Mini-II (i.e. the end of the semester).
If you have an unavoidable conflict with an exam, notify us by filling out “exam conflict” form that we will send out a couple weeks prior to each exam.
No electronic devices are allowed during the exam. Unless otherwise noted, all exams are closed-book.
We will have occasional in-class quizzes, which will always be announced ahead of time. You are required to attend the in-class quizzes.
The homework assignments will consist of both written and programming portions. The written portions will offer opportunities to practice the math / theory / concepts. The programming assignments will offer opportunities to apply those concepts in a concrete setting.
10-606:
10-606 will consist of four homework assignments. They will be identified by Arabic numerals.
10-607:
10-607 will consist of four homework assignments. They will be identified by letters.
Attendance at recitations (Friday sessions) is not required, but strongly encouraged. These sessions will be interactive and focus on problem solving.
The purpose of the readings is to provide a broader and deeper foundation than just the lectures and assessments. The readings for this course are required. We recommend you read them after the lecture. Sometimes the readings include whole topics that are not mentioned in lecture; such topics will (in general) not appear on the exams, but we still encourage you to skim those portions.
We use a variety of technologies:
We will use Piazza for all course discussion. Questions about homeworks, course content, logistics, etc. should all be directed to Piazza. If you have a question, chances are several others had the same question. By posting your question publicly on Piazza, the course staff can answer once and everyone benefits. If you have a private question, you should also use Piazza as it will likely receive a faster response.
We use Gradescope to collect PDF submissions of open-ended questions on the homework (e.g. mathematical derivations, plots, short answers). Upon uploading your PDF, Gradescope will ask you to identify which page(s) contains your solution for each problem – this is a great way to double check that you haven’t left anything out. The course staff will manually grade your submission, and you’ll receive personalized feedback explaining your final marks.
Regrade Requests: If you believe an error was made during manual grading, you’ll be able to submit a regrade request on Gradescope. For each homework, regrade requests will be open for only 1 week after the grades have been published. This is to encourage you to check the feedback you’ve received early!
You will submit your code for programming questions on the homework to Gradescope as well. After uploading your code, our grading scripts will autograde your assignment by running your program on a Docker container. This provides you with immediate feedback on the performance of your submission.
We will also periodically post aggregate grades to Canvas. This provides you a chance to double check that your overall grade is what you expected.
Late homework submissions are only eligible for 80% of the points the first day (24-hour period) after the deadline, 60% the second, 40% the third, and 20% the fourth.
You receive 2 total grace days; they will be applied greedily. No assignment will be accepted more than 4 days after the deadline. You may not combine grace days with the late policy above to submit more than 4 days late.
All homework submissions are electronic (see Technologies section below). As such, lateness will be determined by the latest timestamp of any part of your submission. For example, suppose the homework requires submissions to both Gradescope and Autolab – if you submit to Gradescope on time but to Autolab 1 minute late, you entire homework will be penalized for the full 24-hour period.
In general, we do not grant extensions on assignments. There are several exceptions:
For any of the above situations, you may request an extension by emailing the instructor(s). The email should be sent as soon as you are aware of the conflict and at least 5 days prior to the deadline. In the case of an emergency, no notice is needed.
Formal auditing of this course is permitted. However, we give priority to students taking the course for a letter grade.
You must follow the official procedures for a Course Audit as outlined by the HUB / registrar. Please do not email the instructor requesting permission to audit. Instead, you should first register for the appropriate section. Next fill out the Course Audit Approval form and obtain the instructor’s signature in-person (either at office hours or immediately after class).
Auditors are required to:
We ask that auditors do not submit free-hand assignments (i.e. Gradescope).
We allow you take the course as Pass/Fail. Instructor permission is not required. What grade is the cutoff for Pass will depend on your program. Be sure to check with your program / department as to whether you can count a Pass/Fail course towards your degree requirements.
If you have a disability and have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at access@andrew.cmu.edu.
Read this carefully!
(Adapted from Roni Rosenfeld’s 10-601 Spring 2016 Course Policies.)
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.
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.
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.
All violations (even first one) of course policies will always be reported to the university authorities (your Department Head, Associate Dean, Dean of Student Affairs, etc.) as an official Academic Integrity Violation and will carry severe penalties.
The penalty for the first violation is a one-and-a-half letter grade reduction. For example, if your final letter grade for the course was to be an A-, it would become a C+.
The penalty for the second violation is failure in the course, and can even lead to dismissal from the university.
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:
If you have questions about this or your coursework, please let the instructors know.
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.