syllabus.html

---
layout: page
title: Syllabus
weight: 0
---

<section class="main-container page-head">
    <div class="main">
        <p class="head-subtitle link"><a href="syllabus_2020.pdf" target="_blank">PDF version</a></p>
    </div>
</section>

<section class="main-container text">
    <div class="main">
        <h2 class="title">Course Description</h2>
        <p>This course 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 serve as your map and lodestar as you pursue future
            theoretical and practical directions. </p>

        <h3>Where CS181 fits with other ML/AI options</h3>
        <p>As noted above, our goal is to combine mathematical derivation and coding assignments
        to lead to a strong and rigorous conceptual grounding in the basics 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 and other learning
        theory offerings.  Students interested primarily in practice may
        prefer CS109, CS10, and other data science offerings.  Students
        interested primarily in going deep into a particular machine learning
        or AI topic, rather than a survey, may prefer CS28x and other graduate
        seminars.</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., CS 51 or equivalent).  All staff-provided
        code will be in Python, and the staff will not support questions in
        any other language.  Students should also have a background in basic
        probability theory (e.g.\ STAT 110 or equivalent), and some level of
        mathematical sophistication, including calculus and linear algebra
        (e.g., Math 21a and 21b or equivalent).
        </p>

        <p>
        That said, we note that CS181 only requires portions of all of these
        courses.  Every year, many motivated students are successful in CS181
        without all of these prerequisites.  I want to continue to welcome
        those hard-working students who are willing and able to independently
        fill in any gaps that they might have in their knowledge.  Part I of
            <a href="https://mml-book.com" target="_blank">Math for Machine Learning</a>
            may be 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).
        </p>

        <p>
        <em>Regardless of your background</em>, it is YOUR responsibility to
        learn any prerequisite material on your own.  The course staff will
        not be responsible for teaching basic coding, matrix manipulation,
        etc.}
        </p>

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

        <p>
        <strong>Team</strong>
        The CS181 team consists of a course instructor (responsible for the
        content and grade assignments), a course manager (responsible for the
        logistics of the course---posting things, managing exceptions, etc.),
        and a large staff of TFs lead by a head-TF (responsible for section,
        most of the office hours, and grading).
        </p>

        <p>
            <strong>Lectures</strong>
        Lectures will be used to introduce new content as well as explore the
        content through conceptual questions.  They will occur on the board
        and will not be recorded.  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 both
        homework and midterms.  The teaching staff will introduce the
        questions, assist students in solving them, and wrap up with the
        solutions.  These solutions will be posted. <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>
        The office hour times will be posted on the website.  Please make use
        of office hours!  In addition to getting questions answered by the
        staff, office hours are also a great place to find study partners.
        </p>


        <h3>Materials and Resources</h3>

        <p>
            <strong>Textbook</strong>
        There is no official textbook for the course.  We
        have a version 0.0 of course notes is available
        <a href="https://github.com/harvard-ml-courses/cs181-textbook" target="_blank">here</a>. We emphasize that these notes are due to the awesome effort of a past
        CS181 student who decided to create a course textbook as a senior
            thesis.  <em>Expect errors.</em>  When you spot errors, 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 Piazza and GradeScope.
        </p>

        <p>
            <strong>GradeScope</strong>
        GradeScope will be used for submiting assignments,
        posting grades.
        </p>

        <p>
            <strong>Piazza</strong>
            The piazza site for the course will be used for three purposes:
            <ul>
                <li>
                    <strong>Content</strong> questions are technical questions posted to
                    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 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 clarification questions as "clarification."
                </li>
                <li>
                    <strong>Private Message</strong> There may be times when you wish to
                    send a private messages.  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 Piazza 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.  We will <em>only</em> consider procedural
                    requests via this form (this is as much for you as it is for us: we
                    want to have a record of all requests to hold us accountable).
                </li>
            </ul>
        </p>

        <p>
            Please note that Piazza 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 for which
            such a medium is not appropriate, then please catch Finale in person
            before or after class or catch Finale/TFs at their OHs.  (Note, after
            a conversation, we may still ask you to create a generic request via
            Piazza so we have record of a promised extension, etc.)  Email should
            be used sparingly if at all.
        </p>



        <h2 class="title">Requirements and Grading</h2>
        <p>The main grading components of the course will be the <strong>six homework assignments</strong>
            (<strong>10% each</strong>) and <strong>two midterms</strong> (<strong>20% each</strong>, on March 11 and April 29).
            Participation (in section, office hours, lecture) may be used to bump
            up (but not down) grades that end up near a letter-grade boundary.</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.
        </p>

        <p>
            <strong>Special Circumstances</strong> More broadly, we understand that
        sometimes life throws a set of circumstances that impact your
        perfomance in the course.  Should this happen to you, please let me or
        the course manager know so we can help determine a plan to navigate a
        tough situation.
        </p>

        <h3 id="homework">Homework</h3>
        <p>The homework assignments help you practice the core concepts.  These 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 those gaps. </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.  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> </p>

        <p>
            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
            also an honor code violation to look up solutions 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
        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 clock is slow.
        </p>

        <p>
            You have <strong>five 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!  <em>It is an honor code violation to
            look at the answer key if you haven't yet turned in your
            assignment.</em>
        </p>

        <p>
            The staff will consider giving 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.</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.
        </p>

        <p>
        If you find that you have used up all your late days and have more
        illness, please come talk to Finale.  Similarly, if you have an
        extended sickness, please come talk to Finale.  Most likely, she will
        ask you to start an email with your resident dean to verify their
        support of extra needs.  Note: in none of these cases do you need a
        note from medical; your resident dean provides the abstraction barrier
        between your privacy and what we need to know to appropriately adjust.
        </p>

        <p>
        The only exception is if you have an acute illness at the time of the
        midterm, then you absolutely must get a note from medical at that time
        and send it as soon as possible.  Finale will again likely follow up
        with your resident dean.
        </p>


        <h3>Midterms</h3>
        <p>
            Midterms are a chance to demonstrate your learning individually.
            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, at
            their discretion, provide additional formulas.  <em>Your notes are
            the only resource you are allowed to use during the midterm.  Using
            any other resources, as well as sharing information about the
            midterm to students who are taking it at a different time, is an
            honor code violation.</em>
        </p>



        <h2 class="title">Philosophy</h2>
        <p>
            The goal of this course is to instill a strong technical background
            for you to responsibly apply machine learning in the world.  Thus, in
            addition to the derivations and the practicals, each class will
            include a story about realworld applications of machine learning and
            one full lecture and part of an assignment will be devoted to the
            ethical implications.
        </p>

        <p>
            To be blunt: 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,
            staff, students---to be committed to a open, professional, and
            inclusive environment.  Just like the maths, 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.  I
            welcome constructive feedback to improving the course environment.
        </p>
    </div>
</section>