Syllabus

1. Course Description

This course will cover modern machine learning techniques from a Bayesian probabilistic perspective. Bayesian probability allows one to quantify, model and reason about all types of uncertainty. The result is a powerful, internally-consistent framework for approaching many problems that arise in machine learning, including parameter estimation, model comparison, and decision making.

We will begin with a high-level introduction to Bayesian inference and show how it can be applied to familiar machine learning tasks, such as regression and classification. We will also introduce the workhouse model of the course: Gaussian processes, a flexible non-parametric model with many convenient properties. The second half of the course will cover more-advanced topics, with a heavy focus on probabilistic numerics. The field of probabilistic numerics seeks to apply probabilistic or statistical methods to find numerical solutions for intractable tasks, such as quadrature, global optimization and solving differential equations. Along the way, we will also consider how the techniques presented in class can be a) scaled to handle larger/higher-dimensional datasets and b) combined with active learning techniques to build adaptive models that can determine which data points should be observed next in order to most improve their performance.

The course is designed for students who have completed an introductory course in machine learning.

This course is designed to give PhD students a solid foundation in the methods, mathematics, and algorithms of modern machine learning. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong mathematical and computer science background can catch up and fully participate. If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider the master's level course on machine learning, 10-601. This class may be appropriate for MS and undergraduate students who are interested in the theory and algorithms behind machine learning.

Learning Outcomes

By the end of the course, students should be able to:

• Describe the Bayesian approach to inference and identify potential shortcomings associated with this approach.
• Implement Gaussian process regression and classification.
• Compare and contrast different methods of approximate inference as well as different sampling techniques for dealing with intractable posteriors along a variety of axes, including convergence rates, accuracy and computational cost.
• Perform Bayesian model selection and model averaging to choose between/work with multiple covariance functions for Gaussian process inference.
• Employ both parallelization and sparsification techniques for scaling Gaussian processes.
• Implement a Bayesian optimization routine to optimize the hyperparameters of a machine model.
• Experiment with and analyze the effect of different acquisition functions for Bayesian optimization.
• Analyze the theoretical properties of probabilistic numerical techniques relative to traditional methods on different numerical analysis tasks.
• Extend prototypical probabilistic numerical algorithms to handle common variants of the underlying task (e.g., batch or multi-fidelity settings) and identify settings/applications where said extensions would be appropriate.

2. Prerequisites

Students entering the class are expected to have a pre-existing working knowledge of introductory machine learning by taking one of (10301 or 10315 or 10601 or 10701 or 10715).

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.

3. Recommended Textbooks

This course does not exactly follow any one textbook. However, most lectures will have some recommended readings to help you better understand the material or see a different presentation/perspective. We recommend you read these after the corresponding lecture. These readings will typically be drawn from the following texts, many of which are freely available online:

• Pattern Recognition and Machine Learning; Christopher M. Bishop.
• Gaussian Processes for Machine Learning; Carl Edward Rasmussen and Christopher K.I. Williams.
• Bayesian Optimization; Roman Garnett.

Chapter 6 of the textbook below is a great resource for those hoping to brush up on their probability as we will be making heavy use of those concepts in this course:

• Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.

However, if you find yourself regularly and frequently referring to this textbook, we strongly encourage you to consider whether or not you have the necessary prerequisite knowledge to succeed in this course.

4. Assessments

Your grade in this course will consist of homework assignments, in-class quizzes, a midterm exam and a course project. The breakdown is as follows:

• Homework Assignments = 50%
• In-class Quizzes = 12%
• Midterm = 20%
• Project = 18%
• Deliverable 1 = 6%
• Deliverable 2 = 4%
• Deliverable 3 = 8%

We will convert numerical course grades to letter grades based on grade boundaries that are determined at the end of the semester. The following is a list of upper bounds on the grade cutoffs we will use; in all likelihood, these will be adjusted down at the end of the semester:

• A+: ≥ 97%
• A: ≥ 93%
• A-: ≥ 90%
• B+: ≥ 87%
• B: ≥ 83%
• B-: ≥ 80%
• C+: ≥ 77%
• C: ≥ 73%
• C-: ≥ 70%
• D+: ≥ 67%
• D: ≥ 63%
• R: < 63%

Differences between 10-424 and 10-624

The grading breakdown and content for 10-424 and 10-624 are identical. The only difference between the two courses is that students enrolled in 10-624 must complete an additional homework, HW624. Students in 10-424 will complete four homework assignments and each will account for 12.5% of their grade. Students in 10-624 will complete five homework assignments, the same four homeworks completed by students in 10-424 and HW624; each will account for 10% of their grade.

Homework Assignments

The homework assignments will be divided into two components: programming and written; most assignments will have both components. The programming portions will ask you to implement some of the Bayesian machine learning methods discussed in class from scratch. The written portions will focus on core concepts, “on-paper” implementations, derivations, proofs and understanding of theory. All programming for this course must be completed in Python.

In-class Quizzes

Unless otherwise noted, all quizzes are closed-book. You are required to attend all quizzes in-person. Quizzes will take place at the beginning of some lectures, typically on either Tuesdays; the exact date of each quiz along with the covered material can be found on the lecture schedule.

The quizzes are intended to be a low-stakes assessment, as compared to the exam. As such, when computing your final average quiz grade, we will drop your lowest quiz.

Midterm Exam

The exam for this course will be closed notes; you may bring one sheet of A4 paper as a cheatsheet (both back and front may be used). You are encouraged to handwrite this cheatsheet as a form of preparing for the exam but you may typeset it if you so choose.

The midterm exam will take place on March 20th. 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 either exam (e.g., an exam in another course), notify us as soon as possible by emailing Henry at hchai2@andrew.cmu.edu

Project

The project for this course will be an exploration of Bayesian optimization; a (small) portion of your project grade will be determined by your relative performance against your peers in a competition using a hidden optimization benchmark. Complete details about the course project will be added to this website later in \ the semester.

5. Office Hours

The schedule of office hours will always appear on the course calendar. In general, we will endeavor to hold a minimum of 3 office hours per week, with more in the week leading up to an exam.

We will make use of the following (informal) rules:

• 10 Minute Rule: Each student’s question will be addressed by a course staff member for at most 10 minutes. The only exception to this will be if the question has broad interest to many other students.
• The Pseudo Code Rule: This is not a programming course; you are expected to know how to debug code. As such, if your question is of the form "Could you help me to debug my code?", you must bring with you detailed pseudocode that describes your implementation design. If you do not have pseudocode, the course staff member will not look at your code, but instead ask you to sketch out pseudocode at the chalkboard and discuss there instead. After discussing at a high-level, if your 10 minutes have not expired, the course staff member may have time to look at your code.

While you're awaiting your turn, we encourage you to listen in to the answers to any publicly answered questions. Please be courteous and allow the student who posed the question to primarily direct the discussion. We also encourage you to collaborate with others (following our collaboration policies below) while waiting.

6. General Policies

Late assessment policy

You have a total of 9 grace days for use on any homework assignment or project deliverable except the last project deliverable. We will automatically keep a tally of these grace days for you; they will be applied greedily. You may not use more than 3 grace days on any homework assignment or project deliverable. Submitting an assessment late when you are out of grace days will result in the following penalties:

• Submissions between 0 and 24 hours late will receive a 75% multiplicative penalty.
• Submissions between 24 and 48 hours late will receive a 50% multiplicative penalty.
• Submissions between 48 and 72 hours late will receive a 25% multiplicative penalty.

Extensions

In general, we do not grant extensions on assessments. There are several exceptions:

• Medical Emergencies: If you are sick and unable to complete an assessments or attend class, please go to University Health Services. For minor illnesses, we expect grace days or our late penalties to provide sufficient accommodation. For medical emergencies (e.g., prolonged hospitalization), students may request an extension afterwards.
• Family/Personal Emergencies: If you have a family emergency (e.g., death in the family) or a personal emergency (e.g., mental health crisis), please contact your academic adviser and/or Counseling and Psychological Services (CaPS).
• University-Approved Travel: If you are traveling out-of-town to a university approved event or an academic conference, you may request an extension for any time lost due to traveling. For university approved absences, you must provide confirmation of attendance, usually from a faculty or staff organizer of the event or via travel/conference receipts.

For any of the above situations, you may request an extension by emailing Henry at hchai2@andrew.cmu.edu. Please be specific about which assessment(s) you are requesting an extension for and the number of hours requested. The email should be sent as soon as you are aware of the conflict and at least 24 hours prior to the deadline. In the case of an emergency, no notice is needed.

If this is a medical emergency or mental health crisis, you must also CC your CMU college liaison and/or your academic advisor. Do not submit any medical documentation to the course staff. If necessary, your college liaison and The Division of Student Affairs (DoSA) will request such documentation and they will view the health documentation and conclude whether a retroactive extension is appropriate. If you haven’t interacted with your college liaison before, they are experienced student affairs staff who work in partnership with students, housefellows, advisors, faculty, and associate deans in each college to assure support for students regarding their overall Carnegie Mellon experience.

Audit Policy

Formal auditing of this course is permitted. 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, obtain your academic advisor's signature and then approach the instructor in-person immediately after lecture to obtain their signature.

Auditors are required to:

• Attend or watch all of the lectures.
• Submit at least 2 of the 4 homework assignments (not including HW624).
• Complete the first project deliverable.
• Auditors are encouraged to sit for the in-classes quizzes and the exam, but should only do so if they plan to put forth actual effort in solving them.
• Auditors are also welcome to participate in the other project deliverables, but again should only do so if they plan to invest actual effort in completing them.

Pass/Fail Policy

You are allowed to take this course as Pass/Fail; instructor permission is not required. What letter grade is the cutoff for a Pass will depend on your specific program; we do not specify whether or not you Pass but rather we compute your letter grade the same as everyone else in the class and your program converts that letter grade to a Pass or Fail depending on their cutoff. 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, please email Henry at hchai2@andrew.cmu.edu to set up a meeting for the purposes of discussing your accommodations and needs 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, I encourage you to contact them at access@andrew.cmu.edu.

7. Technologies

We will use a variety of technologies throughout the course:

• Slack: we will use Slack for all course discussion. Questions about homeworks, course content, logistics, etc. should all be directed to Slack. Please be sure to post your question to the correct Slack channel: this is the best way to ensure you get a timely response.

  If you have a question, chances are several other students have the same question. By posting your question publicly, the course staff can answer once and everyone benefits. If you have a private question, you should also use Slack as it will likely receive a faster response. For private questions, you should directly message all course staff members; do not individually message any single course staff member unless you have explicitly received permission to do so.

  Please be considerate and polite when posting in our course Slack, both to each other and to the course staff; rude or hateful speech will not be tolerated and may result in removal from the Slack. Similarly, please be respectful of the course staff's workload when it comes to response times: we cannot guarantee that every question will receive an immediate response, especially for questions posted outside of working hours or near assessment deadlines, when many of your peers might also have questions.

• Gradescope: we will use Gradescope to collect PDF submissions of the homework assignments as well as any code you write to complete the assignments. The course staff will grade your submissions, and you’ll receive personalized feedback explaining the grades you receive.

  If you believe an error was made during grading, you will be able to submit a regrade request on Gradescope. For each homework assignment, 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!

• Panopto: you will be able to watch lecture recordings from our course Panopto folder; note that recordings may not be immediately available after lecture due to editing/processing time.

8. Collaboration and Academic Integrity

Read this section carefully!

Collaboration among Students

The purpose of student collaboration is to facilitate learning, not to circumvent it. Studying the material in groups is strongly encouraged. You are also allowed to seek help from other students in understanding the material needed to solve a particular homework problem, provided any written notes (including code) are taken on an impermanent surface (e.g., whiteboard, chalkboard), and provided learning is facilitated, not circumvented. The actual solution must be written by each student alone.

A good method to follow when collaborating is to meet with your peers, discuss ideas at a high level, but do not copy down any notes from each other or from a shared work surface. Any scratch work done at this time should be your own only. Before writing the assignment solutions, you should make sure that you are doing this without anyone else present, putting all notes away, closing all tabs on your computer, and writing it completely by yourself with no other resources.

You are absolutely not allowed to share/compare answers or screen share your work with one another.

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 answers to 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 Academic Integrity.

Previously Used Assignments

Some of the homework assignments used in this class may have been used in prior offerings, 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 complete 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.

AI Assistance

To best support your own learning, you should complete all graded assignments in this course yourself, without any use of generative artificial intelligence (AI), such as ChatGPT. Please refrain from using AI tools to generate any content (text, video, audio, images, code, etc.) for an assessment. Passing off any AI generated content as your own (e.g., cutting and pasting content into written assignments, or paraphrasing AI content) constitutes a violation of CMU’s Policy on Academic Integrity.

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 pseudocode, 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 in 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 of course policies (even the first one) will always be reported to the university authorities (your department head, associate dean, the 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 negative 100% on the assignment i.e., it would have been better to submit nothing and receive a 0%.
2. The penalty for the second violation is failure in the course, and can even lead to dismissal from the university.

9. Support

Take care of yourself. Do your best to maintain a healthy lifestyle 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/.

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

10. Diversity

We must treat every individual with respect. We are diverse in many ways, and this diversity is fundamental to building and maintaining an equitable and inclusive campus community. Diversity can refer to multiple ways that we identify ourselves, including but not limited to race, color, national origin, language, sex, disability, age, sexual orientation, gender identity, religion, creed, ancestry, belief, veteran status, or genetic information. Each of these diverse identities, along with many others not mentioned here, shape the perspectives our students, faculty, and staff bring to our campus. We, at CMU, will work to promote diversity, equity and inclusion not only because diversity fuels excellence and innovation, but because we want to pursue justice. We acknowledge our imperfections while we also fully commit to the work, inside and outside of our classrooms, of building and sustaining a campus community that increasingly embraces these core values.

Each of us is responsible for creating a safer, more inclusive environment.

Unfortunately, incidents of bias or discrimination do occur, whether intentional or unintentional. They contribute to creating an unwelcoming environment for individuals and groups at the university. Therefore, the university encourages anyone who experiences or observes unfair or hostile treatment on the basis of identity to speak out for justice and support, within the moment of the incident or after the incident has passed. Anyone can share these experiences using the following resources:

• Center for Student Diversity and Inclusion: csdi@andrew.cmu.edu, 412-268-2150
• Ethics Reporting Hotline. Students, faculty, and staff can anonymously file a report by calling 844-587-0793 or visiting cmu.ethicspoint.com.
• username: tartans
• password: plaid

All reports will be documented and deliberated to determine if there should be any following actions. Regardless of incident type, the university will use all shared experiences to transform our campus climate to be more equitable and just.

11. Acknowledgements

This course is built in part using materials by Roman Garnett.

Staff

Instructor

Henry Chai

OH: Tue - 1:30 PM to 2:30 PM in GHC 8133

Teaching Assistants

Schedule

Lectures are the primary mode of content delivery in this course; attending lectures is highly recommended. Lectures will be recorded and made available to you after the fact. However, the primary purpose of these recordings is to allow you to refer back to the content; watching recordings in lieu of attending lectures is not encouraged.

Date	Topic	Notes/Slides	Readings/Resources
Tue, 1/14	Course Overview and the Bayesian Method	Lecture 1 Notes Lecture 1 Supplement	Garnett, Section 1.2 Bishop, Section 1.2
Thu, 1/16	Bayesian Inference: Hypothesis Testing	Lecture 2 Notes	Bishop, Section 2.1
Tue, 1/21	Bayesian Inference: Decision Theory	Lecture 3 Notes	Bishop, Section 1.5
Thu, 1/23	The Gaussian Distribution	Lecture 4 Notes Lecture 4 Supplement	Bishop, Section 2.3
Tue, 1/28	Quiz 1 (Lectures 1 - 4)
	Bayesian Linear Regression	Lecture 5 Notes	Bishop, Section 3.3 Rasmussen and Williams, Section 2.1
Thu, 1/30	Bayesian Logistic Regression & the Laplace Approximation	Lecture 6 Notes	Bishop, Section 4.5 Rasmussen and Williams, Sections 3.1-3.2
Tue, 2/4	The Kernel Trick	Lecture 7 Lecture 7 Supplement	Rasmussen and Williams, Section 2.1.2
Thu, 2/6	Bayesian Model Selection (Pre-recorded Video in lieu of Lecture)	Lecture 8	Garnett, Sections 4.1-4.3 Bishop, Section 3.4
Tue, 2/11	Quiz 2 (Lectures 5 - 8)
	Gaussian Process Regression	Lecture 9 Lecture 9 Supplement	Garnett, Sections 2.1-2.2 Rasmussen and Williams, Sections 2.2-2.3
Thu, 2/13	Gaussian Process Classification & Assumed Density Filtering	Lecture 10	Rasmussen and Williams, Sections 3.3-3.4, 3.9
Tue, 2/18	Expectation Propagation	Lecture 11 Lecture 11 Supplement	Garnett, Appendix B Rasmussen and Williams, Section 3.6
Thu, 2/20	Covariance Functions	Lecture 12 Lecture 12 Supplement	Rasmussen and Williams, Sections 4.1-4.2, 5.4.3 Graph Kernels: A Survey, Nikolentzos et al. (2021)
Tue, 2/25	Quiz 3 (Lectures 9 - 12)
	Bayesian Optimization: Introduction	Lecture 13 Lecture 13 Supplement	Garnett, Sections 7.1-7.5, 8.1-8.3
Thu, 2/27	Bayesian Optimization: Acquisition Functions	Lecture 14 Lecture 14 Supplement	Garnett, Sections 7.7-7.9, 8.4-8.7
Tue, 3/4	No Class (Spring Break)
Thu, 3/6	No Class (Spring Break)
Tue, 3/11	Bayesian Optimization: Variants & Extensions	Lecture 15 Lecture 15 Supplement	Garnett, Sections 2.4, 5.4, 7.6, 11.1
Thu, 3/13	Bayesian Optimization: Variants & Extensions	Lecture 16 Lecture 16 Supplement	Garnett, Sections 11.3, 11.5-11.7
Tue, 3/18	Midterm Review	Annotated Notes
Thu, 3/20	In-class Midterm (Lectures 1 - 14)
Tue, 3/25	Active Search	Lecture 17 Lecture 17 Supplement	Garnett, Section 11.11
Thu, 3/27	Bayesian Quadrature	Lecture 18	Rasmussen and Williams, Sections 9.4, 9.8
Tue, 4/1	Quiz 4 (Lectures 15 - 18)
	Dedicated Project OH
Thu, 4/3	No Class (Carnival)
Tue, 4/8	GP Approximation and Sparse GPs (Guest Lecture by Stephen Huan)
Thu, 4/10	Parallelization of GPs
Tue, 4/15	Ordinary Differential Equations
Thu, 4/17	GPs for Solving ODEs
Tue, 4/22	Quiz 5 (Lectures 19 - 22)
	Sampling Techniques
Thu, 4/24	Bayesian Neural Networks

Recitations

Attendance at recitations is not required, but strongly encouraged. Recitations will be interactive and focus on problem solving; we strongly encourage you to actively participate. A problem sheet will usually be released prior to the recitation. If you are unable to attend one or you missed an important detail, feel free to stop by office hours to ask the TAs about the content that was covered. Of course, we also encourage you to exchange notes with your peers.

Date	Topic	Notes/Handout
Fri, 1/17	Probability Review Recitation	Recitation 1 Notes Recitation 1 Handout
Fri, 1/24	HW1 Recitation	Recitation 2 Handout
Fri, 1/31	No Recitation
Fri, 2/7	HW2 Recitation	Recitation 3 Handout
Fri, 2/14	No Recitation
Fri, 2/21	No Recitation
Fri, 2/28	HW3 Recitation	Recitation 4 Handout Recitation 4 Notebook
Fri, 3/7	No Recitation (Spring Break)
Fri, 3/14	No Recitation
Fri, 3/21	No Recitation
Fri, 3/28	HW4 Recitation
Fri, 4/4	No Recitation (Carnival)
Fri, 4/11	No Recitation
Fri, 4/18	No Recitation
Fri, 4/25	No Recitation

Assignments

Our homework assignments are an opportunity for you all to reason about and build/experiment with some of the models that we introduce in class. All programming questions must be completed in Python and you must use LaTeX to typset your responses to the written questions. You will submit both your code and your written responses using Gradescope; note that each assignment will have separate submissions for the code and the written portion.

Release Date	Topic	Files	Due Date
Thu, 1/23	HW1: Bayesian Inference	HW1 Handout, Overleaf	Thu, 2/6 at 11:59 PM
Thu, 2/6	HW2: Bayesian Linear Regression	HW2 Handout, Overleaf	Thu, 2/20 at 11:59 PM
Thu, 2/20	HW3: Gaussian Process Classification	HW3 Handout, Overleaf	Thu, 3/13 at 11:59 PM
Thu, 3/27	HW4: Bayesian Quadrature	HW4 Handout, Overleaf	Thu, 4/17 at 11:59 PM
Thu, 3/20	HW624	HW624 Handout	Thu, 4/24 at 11:59 PM

Project

Details about the course project can be found in the Project Specifications Document. We will continue to add to this document as additional details become available.

Deliverable	Files	Due Date
Deliverable 1: Baseline Implementation	Project Handout, Overleaf	Thu, 3/27 at 11:59 PM
Deliverable 2: Literature Review	Project Specifications	Thu, 4/10 at 11:59 PM
Deliverable 3: Implementation Write-up		TBD during Final Exam Period

Course Calendar