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 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">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). 
            We have also prepared a Homework Zero [LINK will be added soon] for students to gauge 
            their preparedness or areas they may need to self study.
        </p>

        <h2 class="title">Course Logistics</h2>
        <p>
        <strong>Team</strong>
        The CS181 team consists of the course instructor---Finale Doshi Velez---as 
        well as a large staff of TFs lead by two co-head TFs--Kat Tian and Alex Chin.
        We are all dedicated to helping you to learn the fundamentals of machine learning.
        </p>

        <h3>Lecture, Section, Office Hours</h3>
        <p>
        <strong>Lectures</strong>
        Lectures will be used to introduce new content as well as explore the content 
        through conceptual questions. Students are encouraged to ask questions during 
        lecture, and the instructor will endeavor to hang around after class for 
        lingering questions.
        <!-- 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 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. For example, the sections from Tuesday 
        3/1 to Thursday 3/3 cover content from the lectures on Thursday 2/24 and 
        Tuesday 3/1.
		<!-- <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 office hours both in-person and over Zoom (Link will be added soon).
        Please make use of them!  
<!--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. 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 submitting 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 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 
        CS181: in the subject line. </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>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. <!-- 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 
            (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>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>

        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 single assignment. Start early and plan ahead! The staff 
        will give 50% credit to assignments turned in past their late days at their 
        discretion. 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.
        </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, and all the more so given the continuing pandemic. 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, 
        CS181: 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 (we would not need a doctor's note--especially 
        in these times, there is no reason to additionally burden our medical staff--but 
        rather your resident dean would provide us with what we need to know to 
        appropriately adjust).
        </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. 
        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. </em> <br>

        <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 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---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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