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-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.
- -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.
+ ++ CS 181 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. +
+ ++ The goal of CS 181 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. +
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). Note: knowledge of multivariable calculus is **not required**. +
+ 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).
- Motivated students without all of these prerequisites may also be - able to fill in gaps in their knowledge. Part I of the textbook - Math for Machine Learning - 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 (see HW tab) for students to gauge - their preparedness or areas they may need to self study. + Motivated students without all of these prerequisites may also be + able to fill in gaps in their knowledge. Part I of the textbook + Math for Machine Learning + 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). + Homework zero due January 26 is also a good indicator of your + preparedness and what gaps you may need to fill.
- Team - The CS181 team consists of the course instructor---Finale Doshi-Velez and David Alvarez-Melis---as - well as a large staff of TFs lead by two co-head TFs--Charu Badrinath and Alex Cai. - We are all dedicated to helping you to learn the fundamentals of machine learning. + Team + The CS181 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---Charu Badrinath and Alex Cai. We are all dedicated to helping + you to learn the fundamentals of machine learning.
- Lectures - 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. - - + All learning will be in-person. 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 through the course schedule and the lectures + will follow the course notes.
-- Sections - 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. - + Lectures + 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.
- ++ Sections + 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. +
- Office Hours - We will be holding office hours both in-person and over Zoom (Link will be added soon). - Please make use of them! - + Office Hours + Most office hours will be in person; one set will be over zoom. See the schedule page for information. Please make use of them!
- Textbook - There is no official textbook for the course. There is a set of course - notes available - here. 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. + Textbook + There is no official textbook for the course. There is a set of course notes available here. These notes + come from an awesome effort of a past CS181 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.
- Course Website - 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. + Course Website + 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.
- Gradescope - Gradescope will be used for submitting assignments and posting grades. + Gradescope + Gradescope will be used for submitting assignments and posting grades.
- 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: + Ed + 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: +
- 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, please also directly email the instructor with - CS181: in the subject line. + 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, please also directly email the instructor with + CS181: in the subject line.
The main grading components of the course are the - six homework assignments (10% each), - one practical (10% ), and - two midterms (15% each, 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.
- Grading errors 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. + The main grading components of the course are homework zero (4%), six homework assignments (11% + each), and + two midterms + (15% each, in March and April). + 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. +
+ ++ Grading errors. + 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.
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. -
Submission Policy - 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. +
+ 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. +
+ ++ Submission Policy + 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.
-Collaboration Policy - 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. It is - an honor code violation to copy parts of another person's assignment - (includes text and code) or jointly type up an assignment. - 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). 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). +
+ Collaboration Policy + 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. It is an honor code violation to copy parts of another person's assignment + (includes text and code) or jointly type up an assignment. 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). 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). +
+ ++ Generative AI Policy + 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.
-Late Days Policy 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 six late days that can be used for homework - assignments. Up to two late days 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. 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. - - 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 six late days that - can be used for homework assignments. Up to two late days - 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. +
+ Late Days Policy + + 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 six late days that can + be used for + homework assignments. Up to two late days 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 always in your + interest to + turn in a partial or very 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.
-Sickness (and other Life Events) Policy - 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). +
+ Sickness (and other Life Events) Policy + 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, 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 (doctor's notes are not + necessary; we will discuss solely with your resident dean).
- 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.
- 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.
-
- Illness
- 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.
+ 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. 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.
+ Illness + 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. +
- 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. + 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.
- 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)! + 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)!
- 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. + 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.