CSCI 4845: Natural Language Processing

Saint Louis University · Fall 2026 · Section 01 · CRN 16538 (undergraduate)
Tue/Thu 11:00 AM – 12:15 PM · Busch Student Center 253 · Aug 20 – Dec 3 (last class meeting; regular classes end Fri Dec 4)
Final exam slot: Tue Dec 8, 12:00–1:50 PM — all project presentations run in this slot (no written final)
Instructor: Jin Zhao · Office: Ritter Hall 309 · Email: jin.zhao@slu.edu
Office hours: Tue/Wed/Thu 2:00–3:00 PM in Ritter Hall 309, and by appointment

CSCI 4845 is cross-listed with CSCI 5845-01 (graduate, CRN 16543): same meetings, same lectures and materials, same midterm. Requirements differ in the homework, reading quizzes, and final project (see Assessment). This syllabus states the requirements for CSCI 4845 (undergraduate) students; graduate students should consult the CSCI 5845 syllabus.

Course Description

Natural language processing (NLP) is the study of computational systems that understand and generate human language. This course is a comprehensive tour of the field across its three eras: classical NLP (n-gram language models, text classification, word vectors, sequence labeling, parsing), neural NLP (word embeddings, recurrent networks, attention, the Transformer), and the large language model era (pretraining, alignment and RLHF, prompting, efficient fine-tuning, retrieval-augmented generation, agents, evaluation, and safety). A central theme is that today’s LLMs are the direct descendants of the simplest model we build in week two — the language model — and the course follows that single thread from counting bigrams to reasoning models.

The course is lecture-oriented, with four substantial programming assignments, an in-class midterm, and a final team project presented during finals week.

Audience & Prerequisites

CSCI 4845 is the undergraduate listing of a course that also serves M.S. students in AI and CS (as CSCI 5845); it is intended for advanced undergraduates.

• Required: solid programming ability in Python; comfort with probability, linear algebra, and calculus at the level of an undergraduate course; a prior machine learning course.
• Not assumed: PyTorch experience. Lectures 11–12 include a self-contained neural network refresher (paced via the HW0 background survey), and HW0 walks through the tooling.

Learning Outcomes

By the end of the course, students will be able to:

1. Build and evaluate classical NLP models — n-gram language models, Naive Bayes and logistic regression classifiers, HMM taggers — from scratch, and explain when simple methods remain the right tool.
2. Explain the mathematical machinery of neural sequence models: embeddings, recurrence, attention, and the Transformer architecture, including subword tokenization.
3. Implement and train a small Transformer language model, and fine-tune pretrained models for downstream tasks.
4. Explain how modern LLMs are built end to end: pretraining data and scaling laws, instruction tuning, RLHF/DPO, and inference-time techniques.
5. Apply the modern LLM toolkit — prompting, parameter-efficient fine-tuning, retrieval-augmented generation, and tool use/agents — and justify which approach fits a given problem and budget.
6. Design rigorous evaluations for NLP systems, including benchmark pitfalls, human evaluation, and LLM-as-judge methods, and analyze systems for bias, hallucination, and safety failures.
7. Conduct and communicate an original NLP project: formulate a question, run experiments, and present findings in a conference-style report and talk.

Materials

All required materials are free.

• [SLP3] Jurafsky & Martin, Speech and Language Processing, 3rd ed. draft — https://web.stanford.edu/~jurafsky/slp3/ (primary text for Units 1–2 and parts of Unit 3; chapter numbers in the schedule follow the 2025 draft before the semester).
• [E] Eisenstein, Introduction to Natural Language Processing — secondary reference with stronger mathematical treatment; free PDF at https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf.
• Papers and posts for the LLM-era lectures (linked in the class schedule); roughly one primary reading per lecture in Unit 3.
• Reference courses if you want another angle: Stanford CS224N, CMU 11-711.

Compute: assignments are sized for Google Colab (free tier suffices; Pro is a convenient $10/mo upgrade). HW4 and some projects use LLM APIs — the course will provide API credits (budget ~$5–10/student) and free local alternatives (Ollama, small open-weight models) for every required task.

Course Structure

• Unit 1 — Foundations & Classical NLP (Weeks 1–5, 10 lectures): text processing, n-gram LMs, classification, word vectors, sequence labeling, parsing.
• Unit 2 — Neural NLP & the Transformer (Weeks 6–8, 5 lectures + midterm): neural nets, RNNs, seq2seq and attention, machine translation, the Transformer, tokenization.
• Unit 3 — Pretraining & the LLM Era (Weeks 9–15, 11 lectures): BERT and GPT, scaling, alignment, prompting, efficiency, RAG, agents, evaluation, safety, frontier topics.

See the class schedule for the lecture-by-lecture calendar with readings.

Assessment

Letter scale: A ≥ 93, A− ≥ 90, B+ ≥ 87, B ≥ 83, B− ≥ 80, C+ ≥ 77, C ≥ 73, C− ≥ 70, D ≥ 60, F < 60.

CSCI 4845 requirements (vs. CSCI 5845). The two listings share lectures, homework deadlines, and the midterm. CSCI 4845 requirements differ from the graduate listing in three places:

• Homework: each of HW1–HW4 includes a marked graduate extension (the “G part”). For CSCI 4845 students the G part is optional extra credit (up to +3 points per homework); your homework grade is based on the core parts (out of 100).
• Reading quizzes: CSCI 4845 quizzes consist of the core multiple-choice questions only.
• Final project: CSCI 4845-only teams may choose either track — research/reproduction or application (see the project page). Mixed teams are welcome, but any team that includes a CSCI 5845 student follows the research/reproduction track requirements.

Policies

Attendance. Lecture is the core of the course and slides are not a substitute. Attendance is not directly graded, but in-class participation feeds the participation grade.

Late work. Each student has 5 free late days for the semester, at most 2 per assignment, applied automatically. Beyond that, 10% off per day, and no submissions more than 4 days late. Late days do not apply to the project proposal, presentation, or final report (hard deadlines because they gate feedback and scheduling).

Collaboration. Discussing ideas is encouraged; sharing or copying code or written answers is not. Each submission lists everyone you discussed it with.

AI tools policy. This is an NLP course — using language models thoughtfully is a course topic, not a loophole. Three tiers, stated on every assessment:

• Tier A — Required: parts of HW4 and many projects require using LLMs/APIs.
• Tier B — Permitted with disclosure (default for homework): you may use AI assistants for homework the way you’d use a colleague — to explain concepts, debug, or draft — provided
  1. you disclose the tool and how you used it, and (2) you can explain every line you submit. Instructors may ask you to walk through your solution; inability to explain it is treated as an integrity violation. Core “from scratch” functions flagged in each assignment must be your own typing.
• Tier C — Prohibited: the in-class midterm (closed book, one handwritten note sheet) and reading quizzes.

The assessment mix (in-class exam + oral project presentation + explainability rule) is deliberately designed so the course measures what students can do, not what their tools can do. (This section also serves as the written generative-AI use policy that SLU requires every course syllabus to state explicitly.)

Academic integrity. The University’s required statement appears in University Syllabus Statements below; suspected violations in this course are handled under the University-level Academic Integrity Policy.

Accommodations, Title IX, wellness. See the required and recommended University statements in University Syllabus Statements below.

University Syllabus Statements (AY26-27)

The statements below are the official SLU institution-level syllabus statements, effective August 2026 – July 2027, taken verbatim from the Provost’s compiled statements document (Title IX statement revised 8/13/26; others unchanged from last year).

Academic Integrity (required)

Academic integrity is the commitment to and demonstration of honest and moral behavior in an academic setting. Since the mission of the University is “the pursuit of truth for the greater glory of God and for the service of humanity,” acts of integrity are essential to its very reason for existence. Thus, the University regards academic integrity as a matter of serious importance. Academic integrity is the foundation of the academic assessment process, which in turn sustains the ability of the University to certify to the outside world the skills and attainments of its graduates. Adhering to the standards of academic integrity allows all members of the University to contribute to a just and equitable learning environment that cultivates moral character and self-respect. The full University-level Academic Integrity Policy can be found on the Provost’s Office website at: https://www.slu.edu/provost/policies/academic-and-course/academic-integrity-policy.pdf.

Disability Accommodations (required)

Students with a documented disability who wish to request academic accommodations must formally register their disability with the University. Once successfully registered, students also must notify their course instructor that they wish to use their approved accommodations in the course.

Please contact the Center for Accessibility and Disability Resources (CADR) to schedule an appointment to discuss accommodation requests and eligibility requirements. Most students on the St. Louis campus will contact CADR, located in the Student Success Center and available by email at accessibility_disability@slu.edu or by phone at 314.977.3484. Once approved, information about a student’s eligibility for academic accommodations will be shared with course instructors by email from CADR and within Banner. Students who do not have a documented disability but who think they may have one also are encouraged to contact CADR. Confidentiality will be observed in all inquiries.

Title IX (required)

Saint Louis University and its faculty are committed to supporting our students and seeking an environment that is free of bias, discrimination, and harassment. If you have encountered any form of discrimination on the basis of sex, including sexual harassment, sexual assault, stalking, domestic or dating violence, we encourage you to report this to the University. If you speak with a faculty member about an incident that involves a Title IX matter, that faculty member must notify SLU’s Title IX Coordinator that you shared an experience relating to Title IX. This is true even if you ask the faculty member not to disclose the incident. The Title IX Coordinator will then be available to assist you in understanding all of your options and in connecting you with all possible resources on and off campus.

If you are pregnant or experiencing a pregnancy related condition, the Title IX Coordinator can assist you in understanding your rights and options as well as provide supportive measures.

Anna Kratky is the Title IX Coordinator at Saint Louis University (DuBourg Hall, room 36; anna.kratky@slu.edu; 314-977-3886). If you wish to speak with a confidential source, you may contact the counselors at the University Counseling Center at 314-977-TALK or make an anonymous report through SLU’s Integrity Hotline by calling 1-877-525-5669 or online at SLU.EDU/INTEGRITYHOTLINE. To view SLU’s policies, and for resources, please visit the following web address: https://www.slu.edu/about/safety/sexual-assault-resources/index.php.

Student Success Center

The Student Success Center (SSC) supports students in reaching their goals in and out of the classroom. Providing a variety of resources, the Student Success Center houses both the Center for Accessibility and Disability Resources (CADR) and Academic Support, which includes Tutoring, Supplemental Instruction, University Writing Services, and Student Success Coaching. The Student Success Center is located in the Busch Student Center, Suite 331, and students can make an appointment with any SSC resource via EAB Navigate - Student. To learn more about the Student Success Center and its resources, please visit: https://www.slu.edu/life-at-slu/student-success-center/index.php.

University Writing Services

University Writing Services offers one-on-one consultations with trained writing consultants who help with everything from brainstorming, outlining, and proposing research questions to documenting sources, revising, and implementing feedback. These consultations are available to both graduate and undergraduate students and can take place in-person, asynchronously, or via Zoom. All appointments can be scheduled through EAB Navigate – Student. Getting feedback benefits writers at all skill levels on different writing projects (including but not limited to class assignments, conference papers, cover letters, dissertations, group projects, multimedia assignments, personal statements, senior capstone projects, short answer questions on applications, speeches, and theses). For additional information, visit https://www.slu.edu/life-at-slu/student-success-center/academic-support/university-writing-services/index.php or send an email to writing@slu.edu.

University Counseling Center

The University Counseling Center (UCC) offers free, short-term, solution-focused counseling to Saint Louis University undergraduate and graduate students. UCC counselors are highly trained clinicians who can assist with a variety of issues, such as adjustment to college life, troubling changes in mood, and chronic psychological conditions. To make an appointment for a wellness consultation, call 314-977-8255 (TALK), visit the UCC website or walk into the clinic on the second floor of Wuller Hall. For after-hours needs, please press #9 after dialing the clinic number and ask for a mental health specialist. Free virtual teletherapy is also available through UCC partner, TimelyCare.

Wellness

With our Jesuit commitment to cura personalis, the University sees your academic success as connected to your health and well-being and provides resources to support your holistic wellness.

All students experience stressors and challenges at some point, and seeking support is both normal and beneficial. Such challenges may be the result of academic concerns (such as those related to particular assignments or content in a course), or they may be more personal in nature (such as concerns related to relationships, mental health, medical issues, loss, identities, alcohol or drugs, housing or food security, finances, or local/world events, among other things). If you experience these or other difficulties that are impacting your well-being and/or academic work, please consider seeking support from the resources available to you.

• For questions or concerns related to this course, please contact me. I am invested in your success and will support your success in the ways I can.
• Additionally, you have access to the many resources SLU provides in support of your personal wellness. You will find a list of available resources on the Well-being page of the SLU website.

If you or someone you know is experiencing a crisis: please consult Crisis Support and Warning Signs on the University Counseling Center website or call the University Counseling Center at 314-977-TALK (8255) and press #9 to be connected to a behavioral health nurse 24/7.

Basic Needs Security

Students experiencing food insecurity, housing insecurity, and any other challenges that are impacting their personal and/or academic wellbeing are encouraged to contact the Dean of Students Office for support. Students can submit an intake form, email deanofstudents@slu.edu, or call 314-977-9378 to connect with their office. Students may also communicate directly with their instructors about any challenges they are experiencing to receive support and resource referrals.

Weekly Cadence

• Lectures: Tue/Thu, slide-based with worked examples; each Unit 3 lecture opens with a 5-minute “paper of the day” connecting lecture to primary literature.
• Quizzes: short online reading quiz due before roughly one lecture per week (Canvas).
• Homework: released ~every 2–3 weeks, due Thursdays 11:59 PM (see schedule).
• Office hours: Tue/Wed/Thu 2:00–3:00 PM in Ritter Hall 309; project-focused office hours after Oct 20.