10-601BD, Fall 2018
School of Computer Science
Carnegie Mellon University
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. 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.
Learning Outcomes: By the end of the course, students should be able to:
For more details about topics covered, see the Schedule page.
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.
You need to have, before starting this course, significant experience programming in a general programming language. Specifically, you need to have written from scratch programs consisting of several hundred lines of code. For undergraduate students, this will be satisfied for example by having passed 15-122 (Principles of Imperative Computation) with a grade of ‘C’ or higher, or comparable courses or experience elsewhere.
Note: For each programming assignment, we will allow you to pick between Python and Octave (an open source version of Matlab).
You need to have, before starting this course, basic familiarity with probability and statistics, as can be achieved at CMU by having passed 36-217 (Probability Theory and Random Processes) or 36-225 (Introduction to Probability and Statistics I), or 15-359, or 21-325, or comparable courses elsewhere, with a grade of ‘C’ or higher.
You need to have, before starting this course, college-level maturity in discrete mathematics, as can be achieved at CMU by having passed 21-127 (Concepts of Mathematics) or 15-151 (Mathematical Foundations of Computer Science), or comparable courses elsewhere, with a grade of ‘C’ or higher.
You must strictly adhere to these pre-requisites! Even if CMU’s registration system does not prevent you from registering for this course, it is still your responsibility to make sure you have all of these prerequisites before you register.
(Adapted from Roni Rosenfeld’s 10-601 Spring 2016 Course Policies.)
The core content of this course does not exactly follow any one textbook. However, several of the readings will come from the Murphy book (available free online via the library) and Daumé book (only available online). Some of the readings will include new chapters (available as free online PDFs) for the Mitchell book.
The requirements of this course consist of participating in lectures, midterm, final, homework assignments, and readings. The grading breakdown is the following:
Each individual component (e.g. an exam) or the overall grades may be curved upwards at the end. This allows us to ensure that the number of students that receive A’s (including A+, A, A-) is approximately half the number of students that take the midterm exam. The number of B’s (including B+, B, B-) will be approximately two-thirds the number of A’s.
You are required to attend the midterm and final exams. The midterm exam will be given in the evening – not in class. The final exam will be given during the official final exam week.
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.
The homeworks will divide into two types: programming and written. The programming assignments will ask you to implement ML algorithms from scratch; they emphasize understanding of real-world applications of ML, building end-to-end systems, and experimental design. The written assignments will focus on core concepts, “on-paper” implementations of classic learning algorithms, and understanding of theory.
More details are listed on the Coursework page.
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.
You will submit your code for programming questions on the homework to Autolab. After uploading your code, our grading scripts will autograde your assignment by running your program on a VM. This provides you with immediate feedback on the performance of your submission.
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!
All of the above (Autolab, Gradescope) will give you marks for each part of the corresponding assignment. We will also periodically post aggregate grades to Autolab (usually around midsemester grades and final grades). 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 6 total grace days for use on any homework assignment except HW1. We will automatically keep a tally of these grace days for you; they will be applied greedily. No assignment will be accepted more than 4 days after the deadline. This has two important implications: (1) you may not use more than 4 graces days on any single assignment (2) 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 assistant instructor(s) at firstname.lastname@example.org – do not email the instructor or TAs. 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.
Official auditing of the course (i.e. taking the course for an “Audit” grade) is not permitted this semester.
Unofficial auditing of the course (i.e. watching the lectures online or attending them in person) is welcome and permitted without prior approval. We give priority to students taking the course for a letter grade, so auditors may only take a seat in the classroom is there is one available 10 minutes after the start of class. Unofficial auditors will not be given access to course materials such as homework assignments and exams.
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 email@example.com.
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.