syllabus.html

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        <h2 class="title">Course Description</h2>
        <p>CS 181 provides a broad and rigorous introduction to 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>Where CS181 fits with other ML/AI courses</h3>
        <p> The goal of CS 181 is to combine mathematical derivation and coding assignments
        to provide a strong and rigorous conceptual grounding in
        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 and other learning
        theory offerings.  Students interested primarily in practice may
        prefer CS109a and other data science offerings.  Students
      interested in a more advanced, optimization-based orientation
      may prefer CS 183. Students looking for specialized topics may prefer CS28x and other graduate
        seminars.</p>

        <h3>Prerequisites</h3>
        <p>The material is aimed at an <em>advanced
        undergraduate level</em>.  Students should be comfortable with writing
        non-trivial programs (e.g., CS 51, CS 61, or equivalent).  All staff-provided
        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., AM 22a or Math 21ab, 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
            <a href="https://mml-book.com" target="_blank">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).
        This year we are also planning additional homework zero
        style material as well as additional sections throughout the semester
        to help with mathematical background.
        </p>

        <h2 class="title">Course Logistics</h2>
        <h3>Lecture, Section, Office Hours</h3>

        <p>
        <strong>Team</strong>
      The CS181 team consists of two course instructors--
        Finale Doshi Velez and David Parkes ---as well as a large
staff of TFs lead by two
co-head TFs. We are all dedicated to helping you to learn the
        fundamentals of machine learning.
        </p>

        <p>
<strong>Lectures</strong>

        Lectures will be used to introduce new content as well as explore the
        content through conceptual questions.  They will be given over
        Zoom and also recorded, and involve both slides and iPad-based
discussion.

We plan two sessions each week, in the designated class time. The
instructors will endeavor to hang around after class to answer
questions. Students are encouraged to attend live
so that they can ask questions, including over chat.
<em>We recognize that not everyone is comfortable with using their
video camera during class. But we strongly encourage this and would
like 'video on' to be a norm.</em><br>

Attending live lecture is an expectation of CS 181 students. You are expected to attend at least 7 live lectures (~1/3 of lectures) with your camera turned on, unless your circumstances don’t permit.  If you would like to request an attendance exemption, see <a href="https://edstem.org/us/courses/3955/discussion/223627">this Ed post</a> for instructions.<br>
<!--During lecture, I may remind the class
        about upcoming deadlines, clarify points in the homework, and respond
        to questions about upcoming assignments and midterms.  Not all of
        these interactions may make it to the class announcements.  Thus, if
        you miss a lecture, we strongly recommend asking friends about
        anything that was mentioned.-->
        </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 Monday, when a new section begins. Each week’s section covers the previous week's Tuesday and Thursday lectures. So for example, the sections from 3/1 to 3/4 cover content from Tuesday, 2/23 and Thursday, 2/25's lectures.
		<!-- <em>The staff may post
        additional practice questions or pointers to other practice
        resources.  We do not guarantee solutions for these additional
            resources. </em>-->
            </p>

<!--        <p>
        While sections attendance is optional, attendance will be taken and
        strong participation is one way we may choose to decide letter grades
        for students who are near a boundary.  Section is also a great place
        to find study partners!
        </p>-->

        <p>
            <strong>Office Hours</strong>
        We will be holding a lot of office hours on Zoom.  Please make use
        of these office hours! We have in mind structing them with
            different break-out rooms per problem set question or
topic of interest.
<!--In addition to getting questions answered by the
        staff, office hours are also a great place to find study partners.-->
        </p>
<p><strong>Zoom Policy</strong>
We strongly prefer you participate on Zoom with your camera turned on, unless your circumstances don’t permit.

<p><strong>Tablet</strong>
We will expect students to have access to a tablet, to help with
communication with staff and each other during office hours and sections, and have notifed
the Office of Undergraduate Education about this so that they can be
prepared to help if you do not have access to one.

        <h3>Materials and Resources</h3>

        <p>
            <strong>Textbook</strong>
        There is no official textbook for the course.  There is a set
            of course notes available
        <a href="https://github.com/harvard-ml-courses/cs181-textbook"
            target="_blank">here</a>. We should emphasize, though, that these are due to the awesome effort of a past
        CS181 student who decided to create a course textbook as an
            (unusually ambitious!) senior
            thesis. There may still be some bugs, and if you find any
            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 submiting assignments and
        posting grades.
        </p>

        <p>
            <strong>Ed</strong>
            Most communications with the course staff should go via <a href="https://edstem.org/us/join/6mkCz8">Ed</a>
            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.)  <!-- The course staff will <em>not</em>
                    be responsible for immediate responses but will answer when
                    possible; technical questions to TFs should be brought to office
                    hours (or to section when appropriate).-->
                </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, additional late days,
                    regrades; you may also have other concerns that you wish to share.
                    We ask you to send those as <em>private</em> messages on Ed with
                    the <em>appropriate tag</em>: Regrade, Extension, Special Midterm, and
                    Other.  We will be using these tags to make sure that the right
                    people get your request.  Most such  procedural
                    requests should come this way.
                </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
            two co-instructos</em>.
        </p>



        <h2 class="title">Requirements and Grading</h2>
        <p>The main grading components of the course are the
        <strong>six homework assignments</strong>
            (<strong>10% each</strong>), <strong> one
        practical</strong>  (<strong>10% </strong>), and <strong>two midterms</strong> (<strong>15% each</strong>, in March and April).
            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.  Regrade requests are due 1 week after grades are released.
        </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. We also have one "practical" assignment, which can be
	    done with one other student, and that is more
	    open-ended in nature. You will be asked to explore
	    different machine learning algorithms on a particular data
	    set, with a passing grade for beating some baselines and
	    bonuses for an especially creative or successful
	    approach. </p><br>

        <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. <!-- We encourage you to spend
            time thinking about and understanding the homework on your own before
            collaborating with others to practice for the midterms.-->  <em>It is
            an honor code violation to copy parts of another person's assignment
          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>Late Days Policy</strong> Homework should be submitted electronically
        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 at their discretion. <em> It is almost always in
            your interest to turn in partial or late homework rather than not
            turning in any homework at all. 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 when you are
sick.
<!--
        The whole purpose of a general late day policy is to reduce burden on
        the staff (we don't have to adjudicate what is sick enough, what are
        valid reasons e.g. travel, family events, etc. for an extension) as
        well as allow you some privacy around those decisions.
        -->

At the same time, we understand that
        sometimes life throws a set of circumstances that impact your
perfomance in the course, and all the more so given
the current global pandemic and working and studying conditions.
Should this become a problem for you, please
            let the two co-insuctors know, via email, so that we can help determine a plan to navigate a
tough situation.

        If you find that you have used up all your late days, for
        example,  and have
        more illness then please reach out to us.  Most likely, we will
        ask you to start a correspondence with your resident dean to verify their
support of your extra needs (we would not need a
doctor's note, but rather your resident dean would provide
us with what we need to know to appropriately adjust).





        <h3>Midterms</h3>
        <p>
Midterms are a chance to demonstrate what you have
learned. Midterms will be closed-book, timed (1hr 20 mins), and proctored via Zoom breakout rooms.
We’ll have two time windows to handle different time zones and load balance.

<!--
            Section problems, homework, and concept questions are all great
            starting points for study.  You will be allowed to bring in one sheet
            of 8.5 by 11 paper, front and back, as notes and the staff
        may,-->
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. 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.</em><br>

<strong>Illness</strong>
If you have an acute illness at the time of a
        midterm, then you must let the co-instructors know in advance
of the midterm and  get a doctor's  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
            for you to robustly, successfully, and responsibly apply machine learning in the world.  Thus, in
            addition to the derivations and the practical components, each class will
            include some illustrations and discussion of real world
        applications of machine learning. There will also be a lecture
        and part of an assignment
that is devoted to the
            ethical implications of machine learning as part of
     the    Embedded EthiCS program.
        </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--- co-instructors,
            teaching staff, and students---to be committed to a open, professional, and
            inclusive environment.  We want everyone to be comfortable
in the course and empowered to learn. These qualities take
            cultivation and effort.<!--  I will start with the premise that we're all
            decent people trying our best and expect you to do the same. --> We
            welcome constructive feedback to improving the course
            environment and want you to reach out to the two
            co-instructors, or members of the teaching staff, with any
            concerns.
        </p>
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