CS 124: From Languages to Information

CS 124: From Languages to Information

Dan Jurafsky
Winter 2026, Tu/Th 3:00-4:20 in Hewlett 200.

This course (which will not be taught in AY 2026-2027, so take it now!) is a broad introduction to LLMs and other algorithms for dealing with text, speech, and networks, including the tasks of tokenizing, classifying, searching, recommending, and transcribing information. Topics include LLMs themselves as well as logistic regression, information retrieval, collaborative filtering, neural networks, transformers, speech recognition, social networks, pagerank, and other large language model components and tools. This class is the broad undergrad intro to numerous grad classes like cs224N/S/U/C/V, cs246, cs276, cs336, and cs329R/X.

Schedule Ed Discussion


*Special Note*: CS124 will not be taught in Winter 2027 (or anytime in AY 2026-2027) because I will be on sabbatical. So if you need to take cs124 before Winter 2028, you should take it next quarter, Winter 2026! I look forward to seeing you!
Also FYI that cs124 has no enrollment cap, so everyone is admitted!

Course Staff

Class Time

Tuesday and Thursday 3:00-4:20

Attendance

See the note at the top of the page.

Email

Alas, we can't reply to email sent to individual staff members. If you have a question that is not confidential or personal, post it on the Ed Discussion forum! Responses are quicker and you'll also be helping others with the same question! To contact the teaching staff directly, come see us in office hours!

If that is not possible, you can also email (non-technical questions) to the course staff list, [email protected]. For urgent requests: We check the staff email list very frequently, but please don't worry if you don't hear from us right away. We will do our best to get back to you within a day or so. Just make sure to send an email as soon as you have the request so it's timestamped!

If you have a matter to be discussed privately, come to office hours or use [email protected] to make an appointment. For grading questions, please talk to us after class or during office hours.

Class announcements will be on Ed Discussion (although we will occasionally try Canvas and mailing lists). We will assume that everyone reads all announcements.

Honor Code

Since we occasionally reuse homeworks from previous years, we expect students not to copy, refer to, or look at the solutions in preparing their answers. It is an honor code violation to intentionally refer to a previous year's solutions. This applies both to the official solutions and to solutions that you or someone else may have written up in a previous year. It is also an honor code violation to find some way to look at the test set, or to interfere in any way with programming assignment scoring or tampering with the submit script. It's also an honor code violation to use ChatGPT or any automatic coding system to write your code for you. You should use language models like you use a TA, to improve your understanding. You may not paste code directly from an LLM into your programming assignment.

Unlike prior years, students are allowed to collaborate on quizzes. However, you must each do your own work and only discuss after; each person will be uploading their own work in addition to the answers.

CS124 follows the general Stanford policy on generative AI which is that "use of or consultation with generative AI shall be treated analogously to assistance from another person. In particular, using generative AI tools to substantially complete an assignment or quiz (e.g. by entering quiz or assignment questions) is not permitted", just as having someone do your homework or quizzes for you is not permitted.

Textbook
Course Description

This course is a broad introduction to LLMs and other algorithms for dealing with text, speech, and networks online, including the tasks of tokenizing, classifying, searching, recommending, and transcribing information. Topics include LLMs themselves as well as logistic regression, information retrieval, collaborative filtering, neural networks, transformers, speech recognition, social networks, safety and bias, and other large language model components, tools, and issues. This class is the broad undergrad intro to numerous grad classes like cs224N/S/U/C/V, cs246, cs276, cs336, and cs329R/X.

Prerequisites

CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).

Required Work

From Languages to Information is a flipped class with much of the material online. Before class I recorded all the lectures (except 5 live lectures), and you can watch them at home. The weekly quizzes and programming homeworks will be automatically uploaded and graded. Lectures are available in the Modules section on Canvas. Quizzes and homeworks are on Gradescope and github, but you can find them all on this webpage!!
Prerecorded Video Lectures

Most weeks, we will ask you to watch a set of video lectures (2 to 2.5 hours total). Most videos will have some in-video questions embedded in them, which you should answer. You are required to watch the videos but the embedded quizzes are not counted toward the final grade.

In-class Lectures

5 lectures will be live, and are required (except Week 7 Speech lecture is only Strongly Recommended). For all 5 the material will be on the quizzes.

Labs

There are 5 in-class labs are in which we do group problem-solving activities. The labs are required and will be tested on the quizzes, meaning that if you can't make a particular in-person lab, you must still do the exercises at home instead. But Lab 1 and Lab 4 are required to be attended in-class; the other 3 you can do at home. In-person for Labs 2, 3, 5 is worth extra credit.

Automated Review Quizzes

After watching a week's video lectures, we will ask you to answer an open-notes, open-book review quiz (about 5-6 questions) on the content that you just learned. These quizzes are not timed, they are open book, and they may be attempted an infinite number of times. The questions, as well as the options for each question, may change and be randomly selected from a larger pool each time you take a quiz. You will not see your quiz grade/correct answers until after the due date, but the system will take the the score from the last submission of all your infinitely-allowed submissions for the quiz. So if you worry you might have got something wrong, just submit another one! Review Quizzes for each week are due 11:59pm Tuesday of the following week There are no late days for review quizzes. Because of the strict no-late-day policy, we will drop your lowest scoring quiz (i.e. we will only count your best 8 of the 9 quizzes in your final grade).

Can I work with my friends on the quiz? Yes, you can work with your pair programming partner. But you must each do the problem yourselves, and only then discuss with your partner, and you each submit separately (and you will have to show your own work in the "show your work" section when you upload the quiz answers).

Class Participation

You have to watch all lectures, and attendance for the 5 live lectures is required (except for the Speech lecture, which is only Strongly Recommended). The labs are required and we will test material from them on the quizzes, and labs 1 and 4 must be attended in person. However, attendance for labs 2,3,4 is only strongly recommended; you may do them yourself at home if you really cannot come to class. You can get extra credit for class participation and other things by: Coming to labs 2/3/5 in person; particularly helpful answers on the class Ed forum, helping out other students in office hours or labs, being the first person to find typos in the Jurafsky and Martin textbook (not counting bugs in figure or chapter numbering, since those don't appear in the full book), speaking up in the labs. Plus there will be extra credit problems on some of the labs and possibly PAs.

Programming Assignments

7 Python programming assignments. All are due Fridays at 5pm except the last one, PA7, is due on a Wednesday at 5pm.

Programming Assignment Collaboration for PA 1-6: You may talk to anybody you want about the assignments and bounce ideas off each other. And if you want, you can also choose a partner and do pair programming for PA 1-6. Pair programming has many advantages for learning!!! You and your pair-partner can discuss code, but it's important that each of you work on each part of the assignment so that you're comfortable with the whole assignment, since assignments build on each other (and we will test concepts from the assignments on the quizzes). If you choose to pair-program, you should specify in the submission who your partner is. We will use the normal automatic checks for overlap between your code and other students' code who are not your pair partner. You must describe in your writeup exactly who did what in your code.

Programming Assignment Collaboration for PA 7: PA7 is a group homework that must be done in groups. You will work together with your group, and write code together. Groups must be of size 3 or 4. To work in a group of size 2, you must get special permission from the staff. You cannot work by yourself on PA 7, because part of the goal of this homework is to learn to work on group projects. You must describe in your writeup in detail exactly who in your group did what, and who worked on which parts of the assignment/code.

Late homeworks

You have a total of 4 free late (calendar) days to use on programming assignments 0-6. If you are pair programming, late days are still individual (i.e if one of you has used up late days, and one has not, and you submit a homework late one day, only the student without remaining late days will be penalized). You cannot use late days on PA 7. Once late days are exhausted, any PA turned in late will be penalized 20% per late day. Each 24 hours or part thereof that a homework is late uses up one full late day. However, no assignment will be accepted more than four days after its due date.

Readings

This class has a significant amount of textbook reading. Most weeks have around 25 textbook pages. The homeworks and quizzes are based heavily on the readings.

Final grade computation
  • 3% attendance at mandatory lectures and labs
  • 97% homeworks and quizzes:
    • 73% of 97%: homeworks (PAs 1-6 are each worth the same, 9% (ignore the different point values for each homework). PA7 is worth 18%, double the others, PA0 is worth 1%.)
    • 27% of 97%: weekly review quizzes, each identically worth 27%/8, because the lowest quiz is dropped)
Final letter grades

(the numerator will include your extra credit, the denominator does not include possible extra credit (otherwise it wouldn't be extra credit))
  • A+: It is very easy to get an A in this class but hard to get an A+. For an A+ you must do all of the following:
    • Have perfect scores on all the PAs and quizzes
    • Have perfect attendance at 10 Tuesday classes (that means all lectures and labs, i.e., even the non-required labs)
    • Have given at least 5 substantive and helpful answers to students on the class Ed forum
    • Have turned in and gotten credit on extra credit problems on at least 3 of the labs, quizzes, or PAs
  • A: 93% and above of the total points
  • A-: 90% and above of the total points
  • B+: 87% and above of the total points
  • B: 83% and above of the total points
  • B-: 80% and above of the total points
  • C+: 77% and above of the total points
  • C: 73% and above of the total points
  • C- (= Credit): 70% and above of the total points
  • etc.