Syllabus

Table of contents

1. About BMI 702
2. Assigments and Grading
3. Course Culture
4. Attendance
5. Policies

About BMI 702

Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. This introductory course provides a survey of artificial intelligence for biomedical informatics, covering methods for key data modalities: clinical data, networks, language, and images. It introduces machine learning problems from a practical perspective, focusing on tasks that drive the adoption of machine learning in biology and medicine. It outlines foundational algorithms and emphasizes the subtleties of working with biomedical data and ways to evaluate and transition machine learning methods into biomedical and clinical implementation. An important consideration in this course is the broader impact of artificial intelligence, particularly topics of bias and fairness, interpretability, and ethical and legal considerations when dealing with artificial intelligence.

Goals

• Prepare students for advanced courses in data science, machine learning, and statistics by providing the necessary foundation and context
• Empower students to apply computational and inferential thinking to address real-world problems
• Understand artificial intelligence methods from a practical perspective
• Understand best practices in implementing, evaluating, and validating ML methods on biomedical data
• Apply ML methods to key data modalities: clinical data, biomedical networks, text, and images
• Understand the pros and cons of different ML methods to select the right method for a given scenario
• Recognize the problem of bias in biomedical data and ML methods in healthcare
• Understand the concept of fairness in biomedical ML
• Become familiar with ethical considerations for biomedical data and algorithms

Syllabus

The overall structure is as follows. The course comprises 14 weeks. We provide a course overview and introduction to biomedical AI in the first week. The remaining 12 weeks are divided into six modules. The first week in each module is foundational and introduces key machine learning concepts in the area, and the following week covers advanced topics and frontiers of the same area. The final week of the course introduces students to ethical and legal considerations for biomedical AI.

Assigments and Grading

• There are three problem sets in the course. Assignments are released on Fridays at 9:00am EST and are due at 11:59pm EST on Friday (14 days after they are released). Submissions must be made through Canvas.
• Pre-class quizzes open at 9:00am EST on Friday and are due at 2:00pm EST on Thursday (pre-class quizzes close before lectures on Thursday). Quizzes must be completed in Canvas.

Delayed beyond 24 hours of deadline: no credit. In the case of illness/absence, contact the course instructor. We will work with you to make up any missed assignments.

Delayed beyond 24 hours of deadline: no credit

Questions/issues: please contact the course instructor.

Grade Components

Problem Sets

Problem sets are two-week-long assignments that are designed to help students develop an in-depth understanding of both the theoretical and practical aspects of ideas presented in lecture. The primary form of support students will have for problem sets are the office hours we’ll host, and Canvas Discussions. Problems sets must be completed individually.

Pre-Class Quizzes

Pre-class quizzes are weekly assignments that are graded. That is, your score on them does matter and you must complete them individually.

Class Participation (Quick Checks)

Quick checks are short conceptual questions embedded into each lecture, in the form of Google Forms. Quick checks count towards class participation grade. They are meant for you to check your understanding of the concepts that were just introduced. That is, your score on them does not matter, you just need to do them.

Course Culture

Students taking BMI 702 come from a wide range of backgrounds. We hope to foster an inclusive and safe learning environment based on curiosity rather than competition. All members of the course community—the instructor, TAs and students—are expected to treat each other with courtesy and respect. Some of the responsibility for that lies with the staff, but a lot of it ultimately rests with you, the students.

Be Aware of Your Actions

Sometimes, the little things add up to creating an unwelcoming culture to some students. Bear in mind that diversity has many facets, some of which are not visible. Your classmates may have medical conditions (physical or mental), personal situations (financial, family, etc.), or interests that aren’t common to most students in the course. Another aspect of professionalism is avoiding comments that (likely unintentionally) put down colleagues for situations they cannot control. Bragging in open space that an assignment is easy or “crazy,” for example, can send subtle cues that discourage classmates who are dealing with issues that you can’t see. Please take care, so we can create a class in which all students feel supported and respected.

Beyond the slips that many of us make unintentionally are a host of behaviors that the course staff, department, and university do not tolerate. These are generally classified under the term harassment; sexual harassment is a specific form that is governed by federal laws known as Title IX.

Be Respectful

Professionalism and respect for diversity are not just matters between students; they also apply to how the course staff treat the students. The staff of this course will treat you in a way that respects our differences. However, despite our best efforts, we might slip up, hopefully inadvertently. If you are concerned about classroom environment issues created by the staff or overall class dynamic, please feel free to talk to us about it. The instructor particular welcomes any comments or concerns regarding conduct of the course and the staff.

We are committed to creating a learning environment welcoming of all students that supports a diversity of thoughts, perspectives and experiences and respects your identities and backgrounds. If you feel like your performance in the class is being affected by your experiences outside of class (e.g., family matters, current events), please don’t hesitate to come and talk with us. We want to be resources for you.

Attendance

Students must attend all classes unless they have explicit permission from the course instructor. An unexcused absence can affect the participation grade. This Spring, BMI 702 will be run in a in-person format.

• To see when any live events are scheduled, check the Weekly Schedule.
• To see when lectures, discussions, and assignments are released (and due), check the Home Page.

Auditing

Auditing BMI 702 is only permitted with explicit permission by the course faculty. Auditors must not increase the workload for instructors and TAs, or compete with enrolled students for other resources provided for students who are paying tuition, such as space in online classrooms or time during office hours.

If you are planning to audit the course but want to get more involved, i.e. submit homework assignments, use office hours, etc., you are encouraged to register as a “special student”, which provides access to this course at a per credit cost.

Office Hours

• The office hours are listed on the Weekly Schedule, and will be held both virtually and in-person.
• Students can come to office hours for any questions on course assignments or material.
• In-person office hours will be held in various locations specified in the Weekly Schedule.
• The instructor will also be hosting office hours. These will be reflected on the Weekly Schedule.

Regrade Requests

Students will be allowed to submit regrade requests for the autograded and written portions of assignments in cases in which the rubric was incorrectly applied or the autograder scored their submission incorrectly.

Regrade requests will not be considered in cases in which:

• a student submits incorrect files and the student does not notify the course staff before the assignment deadline
• a student fails to save their notebook before exporting and uploads an old version to the online system
• a situation arises in which the course staff cannot ensure that the student’s work was done before the assignment deadline

Policies

We Want You to Succeed!

You are more than welcome to visit our office hours and talk with us. We know graduate school can be stressful and we want to help you succeed.

Late Policy

Extensions are only provided in the case of exceptional circumstances. For that, email the course instructor to request an extension. If you make a request close to the deadline, we can not guarantee that you will receive a response before the deadline. Additionally, simply submitting a request does not guarantee you will receive an extension. Even if your work is incomplete, please submit before the deadline so you can receive credit for the work you did complete.

Assignments

Data science is a collaborative activity. While you may talk with others about the homework, we ask that you write your solutions individually in your own words. If we suspect that you have submitted plagiarized work, we will call you in for a meeting. If we then determine that plagiarism has occurred, we reserve the right to give you a negative full score (-100%) or lower on the assignments in question, along with reporting your offense to the Center of Student Conduct.

Rather than copying someone else’s work, ask for help. You are not alone in this course! The entire staff is here to help you succeed. If you invest the time to learn the material and complete the assignments, you won’t need to copy any answers.

Using Large Language Models (LLMs) and Generative AI

The following policy outlines the guidelines for the use of generative AI and LLMs in student assignments.

• Responsibility for content: Students who use LLMs and generative AI tools in their assignments must take full responsibility for the content they submit. This includes ensuring the accuracy, relevance, and originality of the information provided by these tools.

• Acknowledgment of AI use: Students must clearly acknowledge any use of LLMs or generative AI in their assignments. This acknowledgment should specify the nature and extent of the assistance received from these tools. LLMs and generative AI can be used to enhance the educational experience, and help with ideation and understanding of complex concepts. However, students must perform the critical thinking, analysis, and synthesis of information.

• Ethical use and originality: Students must use these tools ethically, following the principles of academic honesty. The use of AI to plagiarize, misrepresent original work, or fabricate data is strictly prohibited. Students are encouraged to use these tools to inspire and inform their work, not to undermine the learning process.

• Instructor discretion: Instructors may specify assignments where LLMs and generative AI use is particularly encouraged or prohibited, depending on the assignment’s learning objectives.

This policy helps students get ready for a future with AI in jobs and makes sure their education focuses on honesty and learning. Students are encouraged to read this NEJM AI editorial on why we support the use of LLMs and generative AI in BMI 702.

Collaboration Policy and Academic Dishonesty

All work in this course is governed by Harvard Medical School’s academic integrity policies. It is the students’ responsibility to be aware of these policies and to ensure that their work adheres to them both in detail and in spirit. Unless otherwise specified by the instructor, the assumption is that all work submitted must reflect the student’s own effort and understanding. Students are expected to clearly distinguish their own ideas and knowledge from information derived from other sources, including from conversations with other people. When working with others you must do so in the spirit of collaboration, not via a unidirectional transfer of information. Note that sharing or sending completed assignments to others will nearly always violate this collaborative standard. If you have a question about how best to complete an assignment in light of these policies, ask the instructor for clarification.

Students are expected to clearly distinguish their own ideas and knowledge from information derived from other sources, including from collaboration with other people. Specifically, this means that:

• Students must properly cite all submitted work appropriately.
• Unless noted otherwise, students are expected to complete assignments, quizzes, and projects individually, not as teams. Discussion about course content and materials is acceptable, but sharing solutions is not acceptable.
• Even though students are encouraged to consult websites for solutions to coding problems, they may never just copy code.

If you have a question about how best to complete an assignment in light of these policies, ask the instructor for clarification.

Community Standards

Harvard Medical School is committed to supporting inclusive learning environments that value and affirm the diverse ideas and unique life experiences of all people. An equitable, inclusive classroom is a shared responsibility of both instructors and students, and both are encouraged to consider how their own experiences and biases may influence the learning environment. This requires an open mind and respect for differences of all kinds.

Students are encouraged to contact the course director if they are experiencing bias or feel that their learning experience – including a course’s content, manner of instruction, or learning environment – is not inclusive. Program administrators and directors, the Office for Gender Equity, and the Ombuds Office are also available to discuss your experiences and provide support. Additionally, students can utilize Harvard’s Anonymous Reporting Hotline to report issues related to bias.

Academic and Other Support Services

We value your well-being and recognize that as a graduate student you are asked to balance a variety of responsibilities and potential stressors: in class, in lab, and in life. If you are struggling with experiences either in- or outside of class, there are resources available to help. In addition to program leadership, master’s students can contact Kimberly_Lincoln@hms.harvard.edu, HMS Director of Administration and Student Affairs for Master’s Programs and Johanna_Gutlerner@hms.harvard.edu, Senior Associate Dean for Graduate Education, for support.

Wellbeing and Mental Health Services

Counseling and Mental Health Services (CAMHS) is a counseling and mental health support service that seeks to work collaboratively with students and the University to support individuals experiencing some measure of distress in their lives. It provides coverage to students year-round and is included in the student health fee, regardless of insurance, at no additional cost. More information is available on the CAMHS website or by calling the main office at 617-495-2042. Urgent care can be reached 24/7 at 617-495-5711.

CAMHS Care Line: The CAMHS Cares line 617-495-2042 is a 24/7 support line available to Harvard students who have mental health concerns, whether you are in immediate distress or not, on-campus or elsewhere. This the Line can also be used as resource for Harvard personnel who needs advice about a student who may be experiencing a mental health crisis. At all times, including evenings, weekends, and holidays, you can follow the prompts to speak directly with a CAMHS Cares Counselor about an urgent concern or if you just need to talk to someone about a difficult challenge.

TimelyCare, a virtual mental health and wellbeing platform for all Harvard students covered by the Student Health Fee, offers free virtual mental health care including scheduled counseling, psychiatry, and self-care content to support wellbeing and mental health any time. Scheduled therapy appointments are readily available.

Reasonable Accommodations

As an institution that values diversity and inclusion, our goal is to create learning environments that are usable, equitable, inclusive and welcoming. Harvard University complies with federal legislation for individuals with disabilities and offers reasonable accommodations to qualified students with documented disabilities and temporary impairments. To make a request for reasonable accommodations in a course, students must first connect with their local disability office. The HMS Director of Disability Services, Timothy Rogers (timothy_rogers@hms.harvard.edu), is the point of contact for accommodation information for HMS master’s and MD students.

Accommodations are determined through an interactive process and are not retroactive. Therefore, students should contact their local disability office as soon as possible, preferably at least two weeks before accommodations are needed in a course, or immediately following an injury or illness, in order to initiate the accommodation process. Students are strongly encouraged to discuss their access needs with their instructors; however, instructors cannot independently institute individual accommodations without prior approval from the disability office. Student privacy surrounding disability status is recognized under FERPA. Information about accommodations is shared on a need-to-know basis, and with only those individuals involved in instituting the accommodation.