Course Overview

Large datasets pose difficulties across the machine learning pipeline. They are difficult to visualize and introduce computational, storage, and communication bottlenecks during data preprocessing and model training. Moreover, high capacity models often used in conjunction with large datasets introduce additional computational and storage hurdles during model training and inference. This course is intended to provide a student with the mathematical, algorithmic, and practical knowledge of issues involving learning with large datasets. Among the topics considered are: data cleaning, visualization, and pre-processing at scale; principles of parallel and distributed computing for machine learning; techniques for scalable deep learning; analysis of programs in terms of memory, computation, and (for parallel methods) communication complexity; and methods for low-latency inference.


Students are required to have taken a CMU introductory machine learning course (10-301, 10-315, 10-601, 10-701, or 10-715). A strong background in programming will also be necessary; suggested prerequisites include 15-210, 15-214, or equivalent. Students are expected to be familiar with Python or learn it during the course.


There will be no required textbooks, though we may suggest additional reading in the schedule below.

Course Components

The requirements of this course consist of participating in lectures, homework assignments, a mini-project and two exams. The grading breakdown is the following:


You are required to attend all in person exams. The exams will be given during class. Please plan your travel accordingly as we will not be able accommodate individual travel needs (e.g. by offering the exam early).

If you have an unavoidable conflict with an exam (e.g. an exam in another course), notify us by filling out the exam conflict form which will be released on Piazza a few weeks before the exam.


The homeworks will be divided into two components: 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, derivations, and understanding of theory.

Mini Project

Students will make create groups and participate in a Mini-project. Mini-Project details can be found here: Mini-Project. Students who are in 10-805 or are in a group with a student who is in 10-805 must do both mini-projects A and B. Students who are in a team of only 10-605 students will choose one of the two projects they would like to do.


We will use Piazza for class discussions. Please go to this Piazza website to join the course forum (note: you must use a email account to join). We strongly encourage students to post on this forum rather than emailing the course staff directly (this will be more efficient for both students and staff). Students should use Piazza to:

The course Academic Integrity Policy must be followed on the message boards at all times. Do not post or request homework solutions! Also, please be polite.


We use Gradescope to collect PDF submissions of open-ended questions on the homework (e.g. mathematical derivations, plots, short answers). The course staff will manually grade your submission, and you’ll receive personalized feedback explaining your final marks.

You will also submit your code for programming questions on the homework to Gradescope. 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.

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!

Course Staff

Instructional Staff

Ameet Talwalkar

Henry Chai

Daniel Bird

Teaching Assistants

Rahul Dharani
OH: Wednesday 6:00pm - 7:00pm

Kunal Dhawan
OH: Tuesdays 6:00pm - 7:00pm

Nikhil Gupta
OH: Fridays 4:30pm - 5:30pm

Ramya Ramanathan
OH: Thursdays 10:00am - 11:00am

Ruben John Mampilli
OH: Mondays 12:30pm - 1:30pm

Mehak Malik
OH: Thursdays 6:00pm - 7:00pm

Utsav Dutta
OH: Wednesdays 2:30pm - 3:30pm

Preksha Patel
OH: Thursdays 4:00pm - 5:00pm

Cristian Challu
OH: Tuesdays 10:00am - 11:00am

Schedule (Subject to Change)

Date Lecture Resources Announcements
Data Pre-Processing and Visualization, Distributed Computing
Aug 30 Introduction (slides, annotated, video) HW1 Released
Sep 1 Distributed Computing, Spark (slides, annotated, video)
Sep 2 Recitation 1: Introduction to Pyspark and Databricks (slides, video) Lab Notebook
Sep 6 Visualization, PCA (slides, annotated, video) Tutorial on PCA
JL Theorem
Sep 8 JL + t-SNE (slides, annotated, video) HW2 Released
Sep 9 Recitation 2: Linear Algebra Review (slides, video) Lab Notebook
Basics of Large-Scale / Distributed Machine Learning
Sep 13 Distributed Linear Regression (slides, annotated, video) HW1 Due
Sep 15 Distributed PCA and Logistic Regression (slides, annotated, video)
Sep 16 Recitation 3: HW1 Written Solutions (video)
Sep 20 Kernel Approximation (slides, video)
Sep 22 Distributed Trees (slides, video) HW2 Due
Sep 23 Recitation 4: Probability Recap (slides, video) HW3 Released
Sep 27 Hashing Theory, CMS (slides, annotated, video) Hash kernels, I
Hash kernels, II, Count-min sketch
Sep 29 LSH + Cloud Computing (slides, annotated, video) LSH
Sep 30 Recitation 5: Homework 2 Solutions (video)
Oct 4 AWS Guide (slides, video) HW3 Due
Oct 6 Practice Exam 1
Oct 7 Recitation 6: Homework 3 & Practice Exam 1 Solutions (video)
Oct 11 Exam 1
Oct 13 Guest Lecture - Alex Cabrera, CMU (Blindspots / Robustness) (slides, video) HW4 Parts A + B Released
Oct 14 Recitation 7: HW4 Part A Tutorial (video)
Oct 18 Fall Break (No Classes)
Oct 20 Fall Break (No Classes)
Oct 21 Fall Break (No Classes)
Scalable Deep Learning: Training, Tuning, and Inference
Oct 25 Deep learning, DL frameworks, and Hardware (slides, video)
Oct 27 Large-Scale Optimization (slides, video) HW 4 Part A Due
Oct 28 Recitation 8: Tensorflow Tutorial (Recording Only) (slides 1, slides 2, Notebook,video)
Nov 1 Optimization for DL (slides, video)
Nov 3 Parallel/Distributed DL (slides, video) HW4 Part B Due, HW5 Released
Nov 4 Recitation 9: Optimization & Learning Rates (slides, notebook, video)
Nov 8 Inference, Model Compression (slides, video)
Nov 10 Hyperparameter Tuning (slides, video)
Nov 11 Recitation 10: Homework 4 Solutions (video) Mini Project Proposal Due
Nov 15 Neural Architecture Search (slides, video) HW5 Due
Nov 17 Guest Lecture - Krishna Rangasayee (ML on Embedded Edge Devices)
Nov 18 Recitation 11: Homework 5 Solutions
Nov 22 Thanksgiving (No Classes)
Nov 24 Thanksgiving (No Classes)
Nov 25 Thanksgiving (No Classes)
Advanced Topics
Nov 29 Federated Learning (slides, video)
Dec 1 No Lecture Mini-Projects Due
Dec 2 Recitation 12: Exam 2 Office Hours
Dec 6 Course Summary (slides, video)
Dec 8 Exam 2

General Policies

Late Homework Policy

You receive 4 total grace days for use on any homework assignment. We will automatically keep a tally of these grace days for you; they will be applied greedily. No assignment will be accepted more than 2 days after the deadline without written permission from Daniel, or the Professors. You may not use more than 2 grace days on any single assignment.

All homework submissions are electronic. 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 Written and Programming– if you submit your Written on time but your Programming 1 minute late, your 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 Daniel Bird ( 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.

Audit Policy

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. Unofficial auditors will not be given access to course materials such as homework assignments and exams.

Pass/Fail Policy

Pass/Fail is allowed in this class, no permission is required from the course staff. The grade for the Pass cutoff 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.

Accommodations for Students with Disabilities

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 Daniel Bird 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

Academic Integrity Policies

Read this Carefully

Collaboration among Students

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 proactively 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.

Penalties for Violations of Course Policies

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.
  1. 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+.
  2. The penalty for the second violation is failure in the course, and can even lead to dismissal from the university.


This course is based in part on material developed by Virginia Smith, Heather Miller, William Cohen, Anthony Joseph, and Barnabas Poczos.

Previous courses: 10-405/10-605, Spring 2022, 10-605/10-805, Fall 2021; 10-405/10-605, Spring 2021; 10-605/10-805, Fall 2020; 10-405/10-605, Spring 2020.