syllabus.html

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        <h2 class="title">Course Description</h2>

        <p>
            CS 1810 provides a broad and rigorous introduction to the principles of machine learning, probabilistic
            reasoning and decision making in uncertain environments. We will discuss the motivations behind common
            machine learning algorithms, and the properties that determine whether or not they will work well for a
            particular task. You will derive the mathematical underpinnings for many common methods, as well as apply
            machine learning to challenges with real data. In doing so, our goal is that you gain a strong, conceptual
            understanding of machine learning methods that can empower you to pursue future theoretical and practical
            directions.
        </p>

        <h3>How CS1810 fits with other ML/AI courses</h3>
        <p>
            The goal of CS 1810 is to combine mathematical derivation and coding assignments to provide a strong and
            rigorous conceptual grounding in the principles of machine learning (e.g. being able to reason about how
            different methods should behave in different circumstances). Students interested primarily in theory may
            prefer Stat195, CS184, and other learning theory offerings. Students interested primarily in implementation
            and practice may
            prefer CS109a and other data science offerings. There are also a number of undergraduate and graduate
            courses in the CS[1,2]8* AI umbrella for dives into specific topics and applications.
        </p>

        <h3>Prerequisites</h3>
        <p>
            The material is aimed at an advanced undergraduate level. Students should be comfortable with writing
            non-trivial programs (e.g. any course, experience, or willing to self-study beyond CS50). All staff-provided
            scaffolding code will be in Python. Students should also have a background in probability theory (e.g. STAT
            110 or equivalent), and familiarity with calculus and linear algebra (e.g. AM22a, Math 21b, or equivalent).
        </p>

        <p>
            Motivated students without all of these prerequisites may also be
            able to fill in gaps in their knowledge. Part I of the textbook
            <a href="https://mml-book.com" target="_blank" rel="noopener noreferrer">Math for Machine Learning</a>
            is a useful resource for mathematical
            background (specifically Sections 2.1-2.6; 3.1-3.5; 4.1-4.2; 5.1-5.6; 6.3).
            <a href="https://github.com/harvard-ml-courses/cs1810-s25-homeworks/tree/main/hw0" target="_blank"
                rel="noopener noreferrer">Homework zero</a> due January 31 is also a good indicator of your
            preparedness and what gaps you may need to fill.
        </p>

        <h2 class="title">Course Logistics</h2>

        <p>
            <strong>Team</strong>
            The CS1810 team consists of the course instructors---Finale Doshi Velez and David Alvarez-Melis---as well as
            a large staff of TFs lead by two co-head TFs---Sam Jones and Gabe Sun. We are all dedicated to helping
            you to learn the fundamentals of machine learning.
        </p>

        <h3>Lecture, Section, Office Hours</h3>
        <p>
            <em>All learning will be in-person.</em> In terms of lessons learned from the thick of the pandemic,
            please be
            thoughtful of your peers and the course staff by masking if you are feeling mildly ill or have been exposed
            to people that are ill. If you need to stay home, section notes will be available <a
                href="https://docs.google.com/spreadsheets/d/1IjW90adj4vMRlKM8rmJFmF433ZnougpkZWx62JOIlgo/edit?usp=sharing"
                target="_blank" rel="noopener noreferrer">through the course schedule</a> and the lectures
            will follow the course notes.
        </p>
        <p>
            <strong>Lectures</strong>
            Lectures will be used to introduce new content as well as explore the content through conceptual
            questions. Students are highly encouraged to ask questions during lecture -- let us appreciate being live
            and
            in-person in ways that we didn't before the pandemic! The instructors will linger for a few brief questions
            after lecture; more in-depth questions can be addressed at office hours.
        </p>

        <p>
            <strong>Sections</strong>
            Sections will employ a flipped classroom format, in which students will work on questions that will be good
            preparation for homework and the midterms. The teaching staff will introduce the questions, assist students
            in solving them, and wrap up with the solutions. These solutions will be posted.

            The section cycle “restarts” each Tuesday after lecture, when a new section begins. Each week’s section
            covers the previous week's Thursday lecture and this week's Tuesday lecture.
        </p>

        <p>
            <strong>Office Hours</strong>
            Most office hours will be in person; one set will be over zoom. See the <a
                href="https://harvard-ml-courses.github.io/cs1810-web/schedule" target="_blank"
                rel="noopener noreferrer">schedule page</a> for information. Please make use of them!
        </p>

        <h3>Materials and Resources</h3>


        <p>
            <strong>Textbook</strong>
            There is no official textbook for the course. <a href="https://github.com/harvard-ml-courses/cs1810-textbook"
                target="_blank" rel="noopener noreferrer">There is a set of course notes available here.</a> These notes
            come from an awesome effort of a past CS1810 student who decided to create a course textbook as an (unusually
            and awesomely ambitious!) senior thesis, and several years of students and staffs who have volunteered time
            to fix bugs and improve clarity. If you find bugs, please be a good citizen and put in a pull request.
        </p>

        <p>
            <strong>Course Website</strong>
            The course web site will be used for posting section notes and links to assignments, and includes pointers
            to other resources we'll use, including Ed and GradeScope.
        </p>

        <p>
            <strong>Gradescope</strong>
            Gradescope will be used for submitting assignments and posting grades.
        </p>

        <p>
            <strong>Ed</strong>
            Ed. Most communications with the course staff should go via Ed rather than email. In particular, the Ed site
            for the course will be used for three purposes:

        <ul>
            <li>
                <strong>Content</strong> questions are technical questions posted to
                the teaching staff and other students. (Please keep in mind collaboration
                policies when asking about code or solutions.)
            </li>
            <li>
                <strong>Clarification</strong> questions are posts
                to the teaching staff about logistical
                details (Is there really class on XYZ holiday or is that a mistake?)
                or questions about homework phrasing or typos (Should question 1a of
                the homework be asking for the integral of x, not y?). We will
                make every effort to respond to these questions as quickly as
                possible. Tag these questions as "clarification."
            </li>
            <li>
                <strong>Private Message</strong> These may include procedural
                things such as requests for additional time on midterms or additional
                late days; you may also have other concerns that you wish to share.
                Most such procedural requests should come this way.
                (Note: Regrade requests go directly to Gradescope.)
            </li>
        </ul>
        </p>

        <p>
            Ed is not a formally secure, private, or confidential form of communication,
            and what you send may be seen by the entire course staff. If you have a
            sensitive concern, <em>please also directly email the instructor with
                CS1810: in the subject line.</em>
        </p>


        <h2 class="title">Requirements and Grading</h2>

        <p>
            The main grading components of the course are <strong>homework zero (4%), six homework assignments (11%
                each), and
                two midterms
                (15% each, in March and April).</strong>
            If you do not complete homework zero, the course staff will contact you to discuss your participation in the
            course.
            Participation in section, office hours, Ed, and lecture may be used to bump
            up a grade that for a student who ends up near a letter-grade boundary. Similarly, any bonus component of
            the course, such as an exceptionally creative practical solution, will only be a factor for students on
            grade boundaries.
        </p>

        <p>
            <strong>Grading errors.</strong>
            If you believe there has been a grading error, submit a regrade request
            through GradeScope. However, please note that a) we will regrade the entire assignment, which may result in
            your total grade going up or down, and b) we will only allow one regrade request per problem set.
        </p>


        <h3 id="homework">Homeworks</h3>
        <p>
            The homework assignments help you practice the core concepts that we cover in the course. They involve
            components that are theoretical and conceptual and also require some programming. Homework solutions must be
            submitted in LaTeX and will be returned with grades and solutions. Due to the volume of the grading, it may
            not always be possible for the staff to provide detailed feedback. It is your responsibility to look at the
            solutions, identify gaps, and come to office hours to fill in those gaps.
        </p>

        <p>
            <strong>Submission Policy</strong>
            Homeworks will be submitted on Gradescope. You will submit a pdf of your LaTeX'd work, your original tex
            source, and your code. The staff will only assess your work using the pdf---they will not be running your
            code, or looking at your notebooks. Make sure that all relevant plots and numbers are in your write-up! The
            tex source and code will be used to adjudicate possible Honor Code violations (see below) and other
            exceptional circumstances.
        </p>

        <p>
            <strong>Collaboration Policy</strong>
            You may work with others, but your write-up must be entirely written by yourself in your own words. You may
            help each other debug code, but again, the code must be written by you. Include the names of anyone you
            worked with in your write-up. <em>It is an honor code violation to copy parts of another person's assignment
                (includes text and code) or jointly type up an assignment.</em> You can make use of textbooks and online
            sources
            to help in answering questions, but you must cite your sources (and you should be ready to explain your
            answer to a member of the teaching staff). <em>It is an honor code violation to look up solutions to the
                specific questions that we ask from the internet or other sources (e.g. friends from previous
                years).</em>
        </p>

        <p>
            <strong>Generative AI Policy</strong>
            We recognize that generative AI can be a powerful tool, and we want you to figure out how to use it in ways
            that are benefit you. Any generative AI tools should be treated as collaborators.
            That is, your write-up and code must be entirely written by yourself.
            You must disclose if and how you have used generative AI in your homeworks.
            You must take full responsibility for anything you submit and must be able to explain your solutions
            without any aid from peers or tools.
        </p>


        <p>
            <strong>Late Days Policy</strong>

            Late Days Policy. Homework should be submitted electronically by midnight on the due date, via the
            Gradescope course website. This is a strict deadline, enforced by the site, so submit early enough that you
            don't accidentally discover that your local clock is slow. You have <strong>six late days</strong> that can
            be used for
            homework assignments. Up to <strong>two late days</strong> can be used on any assignment. Start early and
            plan ahead! The
            staff will give 50% credit to assignments turned in past their late days. It is <em>always</em> in your
            interest to
            turn in a partial or very late homework rather than not turning in any homework at all. <em>It is an honor
                code
                violation to look at the solutions if you haven't yet turned in your assignment.</em>
        </p>

        <p>
            <strong>Sickness (and other Life Events) Policy</strong>
            In general, we expect you to use your late days first---the whole point is that you can use them for
            anything, no questions asked. At the same time, we understand that sometimes life throws a set of
            circumstances that impact your performance in the course beyond what the late day policy can address. Should
            this become a problem for you, please let the instructor know, via email, so that we can help determine a
            plan to navigate a tough situation (again, CS1810: in the subject line). If you find that you have used up
            all your late days, for example, and have more illness, then please reach out to us. We may ask you to start
            a correspondence with your resident dean to verify their support of your extra needs (doctor's notes are not
            necessary; we will discuss solely with your resident dean).
        </p>


        <h3>Midterms</h3>
        <p>
            Midterms are a chance to demonstrate what you have learned. Midterms will be closed-book and in-person,
            during class. You will be allowed to bring one sheet of 8.5x11 paper of notes (front and back, any font),
            and to the extent possible, we will also provide you with what we think you need to be able to answer the
            question without needing to memorize too many things. <em>It is an honor code violation to communicate with
                anyone about the midterm while you take the midterm, and to communicate in any way with other
                students.</em> You should also be careful not to share information about the midterm with any students
            who need to take a midterm at a different time due to conflicts with other midterms.
        </p>

        <p>
            <strong>Illness</strong>
            If you have an acute illness at the time of a midterm, then you must let the instructor know in advance of
            the midterm, get a doctor's note (this is one case where we would like a note) and send it to us as soon as
            possible. We will likely also follow up with your resident dean and determine the best way to handle the
            situation.
        </p>

        <h2 class="title">Philosophy</h2>
        <p>
            The goal of the course is to instill a strong technical background about the core principles of machine
            learning. These principles will provide a foundation for you to dive more deeply into the theory of existing
            methods and the creation of new methods, if you wish; they will also provide you with core understanding to
            robustly, successfully, and responsibly apply machine learning in the world. In addition to derivations
            and programming components, each class will include some illustrations and discussion of real
            world
            applications of machine learning. There will also be a lecture that is devoted to the ethical implications
            of machine learning as part of the Embedded EthiCS program, and each homework will have elements that
            require you to go beyond the maths.
        </p>

        <p>
            Given the the increasing use of machine learning systems, the users and developers of these systems must
            hold themselves to high professional and ethical standards. One can cause real harm by pursuing a good cause
            via poor engineering choices. Quoting one of our favorite superheroes: with great power (to run any kind of
            analysis) comes great responsibility (to do it properly)!
        </p>

        <p>
            Relatedly, we expect all participants in this course---instructors, teaching staff, and students---to be
            committed to an open, professional, and inclusive environment. We want everyone to be comfortable in the
            course and empowered to learn. These qualities take cultivation and effort. We welcome constructive feedback
            to improving the course environment and want you to reach out to the instructors, or members of the teaching
            staff, with any concerns.
        </p>
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