Mathematical Foundations for Machine Learning
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.
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.
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.
Below is a summary of the background required for these two courses (consistent with the previous offering of the courses):
Please see the instructor if you are unsure whether your background is suitable for these courses.
See office hours on the calendar below.
Dates | Topic | Slides / Notes / Demos |
---|---|---|
8/30 Mon | 1: Overview | pptx (inked) pdf (inked) |
9/1 Wed | 2: Linear Systems | pptx (inked) pdf (inked) |
9/6 Mon | No Class: Labor Day | |
9/9 Wed | 3: Linear Algebra and Linear Regression | pptx (inked) pdf (inked) lec3.ipynb |
9/13 Mon | 4: Linear Regression | pptx (inked) pdf (inked) handout.pdf |
9/15 Wed | 5: More Multivariate Calculus | pptx (inked) pdf (inked) |
9/20 Mon | 6: Calculus & Lagrange Multipliers | pptx (inked) pdf (inked) |
9/22 Wed | 7: Lagrange Multipliers & Probability | pptx (inked) pdf (inked) lagrange.ipynb |
9/27 Mon | 8: Probability | pptx (inked) pdf (inked) |
9/29 Wed | 9: Probability | pptx (inked) pdf (inked) lec9.ipynb (sol.ipynb) |
10/4 Mon | 10: Statistics | pptx (inked) pdf (inked) handout.pdf (sol) |
10/6 Wed | 11: Linear Algebra and PCA | pptx (inked) pdf (inked) handout.pdf (sol) |
10/11 Mon | Exam review | pdf (With Solutions) |
10/13 Wed | No Class: Reading Day | |
10/14 Thu | FINAL EXAM | 1-4 pm, GHC 6115 |
Recitations are on Friday 3:05-4:25 pm, GHC 6115. Recitations might not be recorded. Recitation attendance is recommended to help solidfy weekly course topics. That being said, the recitation materials published below are required content and are in-scope for quizzes and exams.
Dates | Recitation | Slides/Handouts | Code/Demo |
---|---|---|---|
9/3 Fri | Recitation 1 | CoLab (Solutions) | |
9/10 Fri | Recitation 2 | pptx pdf worksheet (Solution) | CoLab (Solution) |
9/17 Fri | Recitation 3 | worksheet (Solution) | CoLab |
9/24 Fri | Recitation 4 | worksheet (Solution) | CoLab (Solution) |
10/1 Fri | Recitation 5 | CoLab (Solution) | |
10/8 Fri | Recitation 6 | worksheet (Solution) |
Quizzes will take in person during a portion of the lecture time. Quizzes will be announced at least two days before the quiz takes place.
The final exam will be on Thursday, Oct. 14 (Mini course exam day), 1-4 pm.
There will be approximately three homework assignments. Homework assignments will often have both written and online components. Follow the instructions in the Piazza post when homework is announced to make sure complete all necessary components. Due dates are tentative for any assignments that haven't been released yet.
Assignment | Link (if released) | Due Date |
---|---|---|
HW 1 (online and written) | Gradescope, hw1_blank.pdf, hw1.zip | 9/20 Mon, 11:59 pm |
HW 2 (online and written) | Gradescope, hw2_blank.pdf, hw2.zip | 10/2 Sat, 11:59 pm |
HW 3 (online and written) | Gradescope, hw3_blank.pdf, hw3.zip | 10/9 Sat, 11:59 pm |
Grades will be collected and reported in Canvas. Please let us know if you believe there to be an error the grade reported in Canvas.
Final scores will be composed of:
Participation will be based on the percentage of in-class polling questions answered:
Correctness of in-class polling responses will not be taken into account for participation grades.
It is against the course academic integrity policy to answer in-class polls when you are not present in lecture. Violations of this policy will be reported as an academic integrity violation. Information about academic integrity at CMU may be found at https://www.cmu.edu/academic-integrity.
There will be a few other means to collect participation points; stay tuned to Piazza for more details.
We convert final course scores to letter grades based on grade boundaries that are determined at the end of the semester. What follows is a rough guide to how course grades will be established, not a precise formula — we will fine-tune cutoffs and other details as we see fit after the end of the course. This is meant to help you set expectations and take action if your trajectory in the class does not take you to the grade you are hoping for. So, here's a rough, very rough heuristics about the correlation between final grades and total scores:
Grades for graduate students will be broken down further with +/- distinctions. See CMU grading polices for more information.
The above heuristic assumes that the makeup of a student's grade is not wildly anomalous: exceptionally low overall scores on exams, quizzes, and assignments will be treated on a case-by-case basis.
Precise grade cutoffs will not be discussed at any point during or after the semester. For students very close to grade boundaries, instructors may, at their discretion, consider participation in lecture and recitation, exam performance, and overall grade trends when assigning the final grade.
Homework assignments:
Aside from this, there will be no extensions on assignments in general. If you think you
really really need an extension on a particular assignment, contact the instructor as soon as possible
and before the deadline. Please be aware that extensions are entirely discretionary and will be granted only
in exceptional circumstances outside of your control (e.g., due to severe illness or major personal/family
emergencies, but not for competitions, club-related events or interviews). The instructors will require
confirmation from University Health Services or your academic advisor, as appropriate.
Nearly all situations that make you run late on an assignment homework can be avoided with proper planning —
often just starting early. Here are some examples:
We encourage you to discuss course content and assignments with your classmates. However, these discussions must be kept at a conceptual level only.
Violations of these policies will be reported as an academic integrity violation. Information about academic integrity at CMU may be found at https://www.cmu.edu/academic-integrity. Please contact the instructor if you ever have any questions regarding academic integrity or these collaboration policies.
If you have a disability and have an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with us as early in the semester as possible. We 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, we encourage you to visit their website.
Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising,
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. 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 almost always 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 have questions about this or your coursework, please let us know. Thank you, and have a great semester.