IAP 2026 Subjects

6.9600 Mobile Autonomous Systems Laboratory: MASLAB §
Level: U
Units: 6
Grading: P/F
Instructors: Miguel Flores-Acton (mfact@mit.edu) Quang Phuc Kieu, EECS Jieruei Chang, EECS John Zhang, EECS Joseph Hobbs, AeroAstro
Sponsor: Prof. Russ Tedrake
Schedule: Lecture: Monday, January 5, 2026 to Friday, January 9, 2026, 10 am to 12 pm, Room 32-141 Lab: Monday, January 5, 2026 to Friday, January 30, 2026, 10 am to 5 pm, Room 38-500 Competition: Thursday, January 29, 2026, 9 am to 5 pm; Room 26-100
MASLAB has MIT’s premier autonomous robotics course for over 25 years! We teach the full stack of autonomous robotics engineering through a hands-on pedagogical model and an emphasis on learning from both theory and practice. Our curriculum includes lessons on mechanical design, electrical integration, computer vision, mapping, localization, and navigation. Each team of two to six students builds a robot using course-provided materials. Few restrictions are placed on teams’ designs as creative and unique designs are highly encouraged. Teams should have members with diverse interests including design, manufacturing, and software development. Enrollment limited.
6.9610 Battlecode §
Level: U
Units: 6
Grading: P/F
Instructors: Jolie Han (joliehan@mit.edu) David Wei, djwei@mit.edu Evan Lin, dune@mit.edu Augusto Schwanz, aschwanz@mit.edu Vincent Zheng, vzheng@mit.edu Davut Muhammetgulyyev, davutm@mit.edu Max Misterka, misterka@mit.edu
Sponsor: Brynmor Chapman
Schedule: Lectures: Monday - Friday, January 5-16, 7-10pm, room 32-155
Battlecode is a game-theory-focused programming competition in Java and Python where students build virtual robots for real-time strategy gameplay. You'll enhance your programming skills, learn techniques for code-controlled robots under a bytecode limit, and collaborate with peers to create intelligent software. The course concludes with a live Battlecode tournament. Basic programming knowledge is recommended. Registration on subject website required.
6.9620 Web Lab: A Web Programming Class and Competition §
Level: U
Units: 6
Grading: P/F
Instructors: Lucas Bautista, lucas_b@mit.edu, Instructor, EECS Abby Chou, abbychou@mit.edu, Instructor, EECS
Sponsor: Ana Bell
Schedule: Lectures: Monday - Friday, 11-3pm, January 5 -16, room 26-100 Office hours: Monday 1/5, Wednesday 1/7, Tuesday 1/13, Wednesday 1/14, Thursday 1/15, Friday 1/16 (hackathon from 7 PM – 1 AM Saturday), Tuesday 1/20, Thursday 1/22 (hackathon from 7 PM – 1 AM Friday), Friday 1/23, Monday 1/26, Tuesday 1/27; room 32-082 Closing Ceremony January 29, 7-10pm, room 32-123
Students form teams of 1-3 people and learn how to build a functional and user-friendly website. Lectures and workshops teach everything you need to make a complete web application from scratch. Topics include version control, HTML/CSS, JavaScript, React, Node.js, databases, authentication, WebSockets, and more. All teams are eligible to enter a competition where websites will be judged by industry experts. Beginners and experienced web programmers welcome, but some previous programming experience is recommended. Students must register at https://portal.weblab.is. Registering via WebSIS does NOT automatically put you on the official class mailing list. Contact weblab-staff@mit.edu for more info.
6.9630 Pokerbots §
Level: U
Units: 6
Grading: P/F
Instructors: Paco Gomez-Paz (pjgomez@mit.edu)
Sponsor: Silvina Hanono Wachman
Schedule: Lectures: Monday - Thursday, 12-1:30pm, room 6-120 Office hours: Monday - Thursday, 2-4pm, room 4-257
Build autonomous poker players and acquire the knowledge of the game of poker. Showcase decision making skills, apply concepts in mathematics, computer science and economics. Provides instruction in programming, game theory, probability and statistics and machine learning. Compete for over $40,000 in prizes!
6.S088 Algorithmic Problem Solving §
Level: U
Units: 3
Grading: P/F
Instructors: Eric Yang (ehyang@mit.edu) Weiming Zhou (willy108@mit.edu), Lecturer, UG Thomas Liu (tsliu@mit.edu), Lecturer, UG Dylan Isaac (disaac@mit.edu), Lecturer, UG Alex Yang (alexyy@mit.edu), Lecturer, UG Allen Wu (allenwu@mit.edu), Lecturer, UG Brian Xue (brianxue@mit.edu), Lecturer, UG Eric Hsu (erichsu@mit.edu), Lecturer, UG Melody Yu (yumelody@mit.edu), Lecturer, UG Rohin Garg (rohin@mit.edu), Lecturer, UG
Sponsor: Brynmor Chapman
Schedule: Lectures: Monday - Friday, January 1/5-1/16, 4-5pm, room 4-163 Problem-solving sessions 5pm-7pm, room 4-163
Each week, we will cover algorithmic techniques and practice coding challenges, with an emphasis on problem-solving. Class format will be a lecture followed by problem-solving sessions. Days will alternate between beginner and advanced lectures on the same topic. Beginner lectures are designed for students without any prior algorithmic knowledge, while advanced lectures are designed for students who are confident in the beginner lecture material. Problem-solving sessions will contain both beginner and advanced problems, designed for students of all levels. Lectures and problem sessions will cover ideas and skills not practiced in Course 6 classes such as 6.1010 and 6.1210.
6.S089 Intro to UX/UI §
Level: U
Units: 3
Grading: P/F
Instructors: Rachel Onwu (ronwu@mit.edu) Krystal Montgomery (mkrystal@mit.edu) - Co-Instructor - EECS & Architecture (6-3 and 4B) Teresa Jiang (teejay@mit.edu) - Co-Instructor - Architecture and Sloan (4 and 15) Gloria Zhu (gloriazh@mit.edu) - Co-Instructor - EECS & Architecture (6-2 and 4B)
Sponsor: Daniel Jackson
Schedule: Lectures: Monday - Friday, 2-4pm. January 5 - 16, 2026, room 56-114 Office Hours: Sunday, 4:00 - 7:00 pm (Jan 11), room 36-112
This course will serve as an introduction to UX (user experience) and UI (user interface) design, giving students the opportunity to learn design principles/methodologies and apply them to a real-world scenario. We aim to follow the design thinking process throughout the course, providing lessons on user research, wireframing, low/high-fidelity prototyping, usability testing, and setting up a portfolio. Each student will be given a case study at the beginning of the course to apply the process practically and to enhance the UI/UX of a digital platform. Classes will follow a lecture and workshop format, to allow students to apply what they learned directly to their projects, receive peer and instructor feedback, and iterate in real time. By the end of the course, students will present a polished prototype and a documented case study suitable for inclusion in a design portfolio.
6.S092 The Art and Science of PCB Design §
Level: U
Units: 3
Grading: P/F
Instructors: Will Vu (willvu@mit.edu), EECS Deepta Gupta, deeptag@mit.edu, EECS Noah Haefner, nhaefner@mit.edu, EECS
Sponsor: Joe Steinmeyer
Schedule: Lectures: Mon/Wed/Fri, 10AM-11AM, January 5-30 room 2-190 Recitation: Mon/Wed/Fri from 11AM-12PM, room 2-190 Office Hours will be Mon/Wed/Fri from 9-10AM and 12PM-1PM. OH room simply needs capacity for 20 people. Labs will be Tues/Thurs from 9AM-3PM in the 38-500 lab space.
The Art and Science of PCB Design is an introductory course into the fundamental aspects of developing electronic systems on printed circuit boards (PCBs). This course will heavily focus on providing hands-on labs with electronic design tools actively used in industry towards designing a primary course project resulting with the physical assembly of a PCB-based device. Students will gain experience in designing systems, conducting SPICE simulations, drawing schematics, and creating a PCB layout. Complex topics in electrical and PCB design will be explored, including from guest speakers and through advanced simulations. This class is intended for students of all skill levels but at a minimum requires a basic understanding of circuit analysis, which will be applied towards learning how to implement real devices.
6.S093 How to ship almost anything with AI §
Level: U
Units: 6
Grading: P/F
Instructors: Artem Lukoianov (arteml@mit.edu) Serge Vasylechko, sergeicu@mit.edu, co-instructor
Sponsor: Justin Solomon
Schedule: Lectures and Workshops: January 20 - 23, 2025 MTWRF 10- 5pm, room 3-333 January 26 - 27 MT 10-4pm, room 37-212 Thursday 12-2pm, room 4-370 Optional Day 5: Monday, Jan 26 10am - 5pm, room 37-212 Final Presentation: Tuesday, January 27, 10-4, room 37-212
Sam Altman predicts that soon there will be a billion-dollar company run by a single person. With agentic AI tools improving every month, it's becoming easier to believe. At Sundai, we've been building with AI every Sunday for the past 100 weeks, and in this class, we'll teach all the technical skills we've learned to build full-stack products single-handedly. Students will build a fully autonomous AI agent that manages social media based on your second brain app of choice. The agent monitors your knowledge base (Notion, Obsidian, or text files), generates contextually relevant posts, creates custom images using fine-tuned diffusion models, and engages with online conversations—all without manual intervention. Topics include agentic system design with tool calling, multimodal AI and image fine-tuning, RAG systems and semantic search, cloud deployment, web scraping, and rapid prototyping with AI-powered developer tools. The course culminates in live demonstrations of working autonomous agents. Enrollment is limited, please apply through iap.sundai.club asap
6.S095 Probability Problem Solving §
Level: U
Units: 6
Grading: P/F
Instructors: Katie Spivakovsky (kspiv@mit.edu), Biological Engineering and EECS Maximus Lu, maxlu@mit.edu, Mathematics Karthik Vedula, kvedula@mit.edu, Mathematics Sitta Tantikul, sittatan@mit.edu, Mathematics Matthew Zhao, mattzhao@mit.edu, EECS Tina Zhang, zhangtin@mit.edu, Mathematics Shiqi Cheng, sqcheng@mit.edu, Year: 3, Department: Mathematics and EECS
Sponsor: Guy Bresler, guy@mit.edu
Schedule: Lectures: Tuesday and Thursdays, January 6-29, 2-5 PM, room 32-141 Recitation: Wednesdays, Fridays 1-3, room 4-370 Office Hours: Mondays and Fridays 3-5 PM, room 4-237 starts 1/23.
6.S095 is a survey of problem solving techniques in probability, random variables, and stochastic processes. It picks up from a standard introduction to the subject and goes towards more advanced techniques. The first half of 6.S095 reviews standard concepts in probability while introducing much more involved applications of these topics, while the second half will introduce adjacent areas of exploration. The aim of this class is to develop problem solving ability and mathematical maturity that will enable students to succeed in advanced and graduate-level EECS classes that involve probability such as 6.1220 (6.046), 6.7710 (6.262), 6.7720 (6.265), 6.7800 (6.437), 6.7810 (6.438), and 6.5220 (6.856). The class runs in two tracks: a standard track that has greater focus on problem solving in fundamental probability concepts, and an advanced track that solidifies problem solving skills in more advanced probability techniques. Each track will have 7 lectures with 7 corresponding recitations and PSets.
6.S097 Ultrafast Photonics §
Level: U
Units: 3
Grading: P/F
Instructors: Phillip Donald Keathley (pdkeat2@mit.edu)
Schedule: Lectures: Tuesdays and Thursdays 11-12:30, January 6- 29, room 35-310
Knowledge of the fundamentals of ultrafast photonics is becoming increasingly valuable as ultrafast optical sources become more ubiquitous with an ever-growing number of applications. Relatively compact ultrafast optical sources with pulse durations ranging from nanoseconds down to femtoseconds are now commercially available across a broad range of wavelengths. Current applications are wide-ranging and include biological imaging, quantum optical technologies, chemical sensing, and precision measurements of time and distance among many others. During this IAP course, we will cover the essentials of ultrafast photonics. Topics will include: (1) the science of ultrafast laser pulses and their interaction with matter; (2) the technology to generate and manipulate ultrafast pulses of light; and (3) an overview of select applications of ultrafast photonics systems. This course will serve as a foundation for those interested in experimental and/or theoretical work involving ultrafast optical systems. Some basic knowledge of Fourier analysis, differential equations, and electromagnetic waves is assumed.
6.S099 Machine Learning Challenge for Biomedical Discoveries §
Level: U
Units: 6
Grading: P/F
Instructors: Caroline Uhler (cuhler@mit.edu) Paul Blainey, Jonathan Weissman
Sponsor: Caroline Uhler
Schedule: Lectures: Tuesday & Thursday, Jan 5-Jan 29, 11.30-1; room 26-168
Scientists are increasingly turning to machine learning challenges, or competitions that require participants to build and evaluate machine learning models over a given period of time to solve a problem. The Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard organizes global machine learning challenges to leverage machine learning for solving key biomedical problems and to help prioritize what experiments biologists could run next – creating the next steps in disease diagnostics and treatment. In this class, students will participate in the Schmidt Center’s machine learning challenge and apply their machine learning skills to help solve a key biomedical problem. Students will learn the basics of genomics and data analysis needed to succeed in the challenge. Top-scoring submissions will be validated in a lab at the Broad Institute, and winners will be eligible for monetary prizes.
6.S183 A Practical Introduction to Diffusion Models -- From Algorithms to Implementation §
Level: U
Units: 3
Grading: P/F
Instructors: Daniel Pfrommer (dpfrom@mit.edu) Chenyang Yuan, ycy@mit.edu Chris Scarvelis, scarv@mit.edu Cole Becker, colbeck@mit.edu Artem Lukoianov, arteml@mit.edu
Sponsor: Justin Solomon
Schedule: Lectures: MWF 10am-11pm, Jan 5 to Fri Jan 16, room 32-144.
We will cover the fundamentals of diffusion models, generative modeling, and applications to different fields. This year the course will also include recent advancements and architectures such as the MeanFlow framework and Recurrent Interface Networks.
6.S184 Generative AI with Stochastic Differential Equations: Introduction to Flow Matching and Diffusion Models §
Level: U
Units: 3
Grading: P/F
Instructors: Peter Holderrieth (phold@mit.edu) Ron Shprints (ronsh@mit.edu)
Sponsor: Tommi Jaakkola
Schedule: Lecture 01: Tuesday, Jan 20, 11am-12:30pm Lecture 02: Thursday, Jan 22, 11am-12:30pm Lecture 03: Friday, Jan 23, 11am-12:30pm Lecture 04: Monday, Jan 26, 11am-12:30pm Lecture 05: Wednesday, Jan 28, 11am-12:30pm Room E25-111 Office hours (OH) 1: Wednesday, Jan 21, 11am-12:30pm, room 36-144 2: Friday, Jan 23, 3pm-4:30pm, room 36-156 3: Tuesday, Jan 27, 11am-12:30pm, room 36-144
Diffusion and flow models are the cutting edge generative AI methods for images, videos, and many other data types. This course offers a comprehensive introduction for students and researchers seeking a deeper understanding of these models. Lectures will teach the core mathematical concepts necessary to understand diffusion models, including stochastic differential equations and the Fokker-Planck equation, and will provide a step-by-step explanation of the components of each model. Labs will accompany each lecture allowing students to gain practical, hands-on experience with the concepts learned in a guided manner. At the end of the class, students will have built a latent diffusion model from scratch – and along the way, will have gained hands-on experience with the mathematical toolbox of stochastic analysis that is useful in many other fields. This course is ideal for those who want to explore the frontiers of generative AI through a mix of theory and practice. We recommend some prior experience with probability theory and deep learning. More information can be found here https://diffusion.csail.mit.edu/
6.S187 Deploying Generative AI: Healthcare, Business Analytics, Education, Innovation, Investment §
Level: U
Units: 3
Grading: P/F
Instructors: Manolis Kellis (manoli@mit.edu) Pascal Passingan (ppxscal@mit.edu) Raymond Bahng (reugene@mit.edu) Jinha Kim (jinhakim@mit.edu) Jophy Ye (jophyyjh@mit.edu) Soham Samanta (soham2020sam@gmail.com) Krishiv Thakuria (krishivthakuria@gmail.com) Arjun Kulkarni (arjun.kulkarni@shopatshowcase.com) Serge Vasylechko (sergeicu@mit.edu) Lauren Pearson (l.pearson@mail.utoronto.ca) Aditya Sengupta (adityasngpta@gmail.com)
Schedule: Lecture: M,T,W,R,F at 4pm-5pm, January 12-16, room 32-155 Group work in teams: M,T,W,R,F at 5pm-7pm, room 32-044 Deployment, fine-tuning, agentic workflows, business model, 7-9pm, room 32-141 Presentations, Pitches, 9-10pm, room 32-141
This course introduces students to the real-world deployment of generative AI systems across diverse domains, from healthcare and education to finance, policy, law, and innovation. Students will explore how to construct semantic maps and agentic workflows that operate over multimodal data — including scientific literature, legal rulings, patient records, financial reports, genomic data, and startup portfolios — and will build AI-powered solutions for search, summarization, exploration, and decision-making. Through domain-specific modules, students will learn to model biomedical knowledge, encode corporate structure and task flows, build educational tutors, analyze legal and regulatory frameworks, and visualize financial or patent risk using embedded reasoning. Each day focuses on a vertical, teaching students to ingest real datasets, deploy agents for complex queries, and compose workflows tailored to the needs of experts in science, healthcare, law, government, investment, or industry. Hands-on workshops will use the Mantis AI platform ( https://mantisdev.csail.mit.edu/ ) to guide students from ingestion to agentic insight. Topics include: vertical-specific embeddings, agent orchestration and policy modeling, multimodal hypothesis generation, real-time document analysis, and semantic decision graphs. Suitable for students seeking to apply AI to societal challenges, organizational coordination, scientific discovery, and product innovation. No prior AI experience required, but Python and React comfort is recommended. Enrollment may be limited. Please indicate your interest here tinyurl.com/GenAIcourseRSVP See also 6.S189. Three units each subject, 6 units for both 6.S187 and 6.S189.
6.S188 Build a Digital Clock From the Eighties §
Level: U
Units: 3
Grading: P/F
Instructors: Joe Steinmeyer, jodalyst@mit.edu, Senior Lecturer, EECS Dept Adam Hartz, hartz@mit.edu, Senior Lecturer, EECS Dept
Schedule: Lectures: Tues/Thursdays, 11-12:30, January 6 -29, room 34-301 Labs in 38-501/601
Class investigates very simple digital logic (gates, counters, etc...) and related concepts towards the end goal of making a TTL and CMOS-based desk clock using 74-series logic chips (like one would have done in the 1970s or 1980s or 1990s). The first two weeks will have basic labs, and the last two weeks will involve designing and assembling the clock. If students wish (and schedule-permitting), clock designs can be done as a printed circuit board (PCB) which will be sent away for, otherwise designs can be done on breadboard or wire-wrapped or hand-soldered.
6.S189 Foundations and Frontiers of Generative AI, Cognitive Cartography, Knowledge Graphs, and Agentic Workflows §
Level: U
Units: 6
Grading: P/F
Instructors: Manolis Kellis (manoli@mit.edu) Pascal Passigan (ppxscal@mit.edu) Raymond Bahng (reugene@mit.edu) Jinha Kim (jinhakim@mit.edu) Jophy Ye (jophyyjh@mit.edu) Soham Samanta (soham2020sam@gmail.com) Krishiv Thakuria (krishivthakuria@gmail.com) Arjun Kulkarni (arjun.kulkarni@shopatshowcase.com) Serge Vasylechko (sergeicu@mit.edu) Lauren Pearson (l.pearson@mail.utoronto.ca) Aditya Sengupta (adityasngpta@gmail.com)
Schedule: Lecture: M,T,W,R,F at 10am-12pm, room 35-225, January 5-9. Lunch Discussion/Brainstorming/Questions: 12pm-1pm, room 35-225 Workshop: M,T,W,R,F at 1pm-5pm, room 35-225 Optional Break/Discussion: 5pm-6pm, room 35-225 Friday only: Optional pizza & presentations 6-7pm. 35-225
This course introduces students to the emerging paradigm of cognitive cartography: interactive, AI-driven maps of knowledge across natural language, images, knowledge graphs, geometry, chemistry, molecules, protein structure, and other modalities. Students will explore the foundations of semantic embeddings, conceptual clustering and ontologies, entity recognition and knowledge graph extraction, agentic orchestration and tool utilization, LLM fine-tuning, and workflow inference, while also learning about the foundations and frontiers of representation learning, multi-modal embeddings, hierarchical concept maps, reinforcement learning, agentic reasoning, and AI scaling laws and deployment. Topics include: Visual AI, knowledge graphs, agent collaboration, MCP protocols, graphical data representation and reasoning, and hypothesis-driven analytics. Students will participate in hands-on workshops using the Mantis AI platform (https://mantisdev.csail.mit.edu/), and teaching staff will guide students from data exploration to composable workflows. Suitable for students interested in AI, data science, human-AI collaboration, and computational thinking across disciplines. No prior AI experience required, but Python comfort recommended. Enrollment may be limited. Please indicate your interest here tinyurl.com/GenAIcourseRSVP See also 6.S187. Three units each subject, 6 units for both 6.S187 and 6.S189.
6.S190 Computational Proteomics: Models, Structures, and Simulations §
Level: U
Units: 3
Grading: P/F
Instructors: Matias Vergara (matiasv7@mit.edu) Krithik Ramesh; coinstructor; EECS 6-4
Sponsor: Prof. Bonnie Berger
Schedule: Lectures: Monday-Wednesday, 3-4pm, January 5 - 29, room 36-144.
This intensive short course gives a focused, practice-oriented tour of modern computational proteomics, emphasizing how core models are built and used. We proceed from protein language models to structure prediction, docking/PPIs, and generative design, with weekly hands-on work.
6.S191 Introduction to Deep Learning §
Level: U
Units: 3
Grading: P/F
Instructors: Daniela Rus (rus@mit.edu) Alexander Amini (amini@mit.edu) Ava Amini (asolei@mit.edu)
Sponsor: Daniela Rus
Schedule: Lectures: Monday - Friday, 1-4pm from January 5 - 9, Room 32-123.
Introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow and PyTorch. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors.
6.S192 Agentic Web: Networked AI Agents and Decentralized AI (NANDA) §
Level: U
Units: 3
Grading: P/F
Instructors: Shoumik Chowdhury (shoumikc@mit.edu) - Leader - EECS Shantanu Jha (shanjha@mit.edu) - Leader - EECS Ramesh Raskar (raskar@mit.edu) - Lecturer - MAS Pradyumna Chari (pchari@mit.media.edu) - Lecturer - MAS Serge Vasylechko (sergeicu@mit.edu) - Lecturer - MAS Maria Gorskikh (mgors@mit.edu) - Lecturer - MAS
Sponsor: Philip Isola
Schedule: Lectures: Monday - Friday, 10am-12pm, January 26 -30, room 66-168 12pm-1pm - lunch. Interactive workshop: 1pm-4pm, room 66-168
This hands-on interactive IAP course will teach students how to architect and deploy autonomous AI agent systems that plan, coordinate, and execute across distributed web applications. Students will learn how to master AI agent design patterns, communication protocols, web automation frameworks, and decentralized coordination mechanisms. The hands-on interactive sessions will be in the form of tutorials where students will build AI agent applications based on their own personal interests - and ship working prototypes to try in day to day environments. The goal is to make this course experiential and fun for students who are interested in AI agents. Learning goals: - To learn how to design and implement AI agents that communicate via A2A and MCP protocols. - To learn how the architecture and standards of the emerging agentic web ecosystem are built. - To build and deploy multi-agent systems with security, discovery, and coordination. - To learn how to evaluate trade-offs in agent design for real-world applications Key Topics are covered in 10 interactive tutorials: Sessions 1-2 - Foundations of Agentic Web - introduction, protocols and communication standards Sessions 3-5 - Core infrastructure - identity/security/trust, agent capabilities and discovery, multi-agent coordination Sessions 6-7 - Advanced Environments - execution environments and sandboxes, agentic colearning Session 8 - Sandbox - working with agent sandbox environments and experimenting with protocols Sessions 9-10 - Deployment - economic models and building applications, real-world use cases and research frontiers
6.S912 Designing Social Software §
Level: U
Units: 3
Grading: P/F
Instructors: Theia Henderson (tfh@mit.edu) David Karger, karger@mit.edu, Co-lecturer, CSAIL
Sponsor: David Karger, karger@mit.edu
Schedule: Lectures: Monday/Wednesday/Friday, January 5 - 30, 1-3pm, room 36-112
Much of the software we use today is social, from Facebook to WhatsApp to Wikipedia to Google Docs to Tinder to Minecraft. This course will guide students through designing and building their own social software. To support this work, we will overview topics from the field of social computing including moderation, anonymity, recommender systems, crowd sourcing, context collapse, misinformation, decentralization, and more. We will explore these topics through both studies of existing online communities and emerging social computing systems research. Basic experience with front-end web design is recommended, but not required.
6.S913 Fundamentals of Linux Systems §
Level: U
Units: 3
Grading: P/F
Instructors: Juni Kim (junickim@mit.edu)
Sponsor: Nickolai Zeldovich
Schedule: Lectures: Tuesday 01/20, Wednesday 01/21, Friday 01/23, Monday 01/26, Thursday 01/29, 1-3 PM, Room 34-301 Labs: Wednesday 01/21/2026 3-5, room 34-301 Friday 01/23/2026 3-5, room 34-301 Tuesday 01/27/25, 1-5, room 34-301 Thursday 1/29/25, 3-5, room 34-301
A hands-on introduction to the fundamentals of modern Linux systems. Topics include the boot process (bootloader, kernel, initramfs), system configuration, userland filesystems, users and groups, and essential daemons. Students gain experience with GCC, QEMU, BusyBox, kernel compilation, and constructing a minimal Linux-based operating system. Intended for undergraduates in Computer Science, Electrical Engineering, or related fields seeking practical exposure to operating systems and some amount of low-level software engineering. Prerequisites: comfort with Unix/Linux environments and familiarity with command-line tools; prior systems coursework helpful but not required. Coursework centers on lab-based assignments, culminating in building a functioning Linux system image.
6.S914 Machine Learning for Neural Interfaces §
Level: U
Units: 3
Grading: P/F
Instructors: Andrii Zahorodnii (zaho@mit.edu)
Sponsor: Prof. Boris Katz
Schedule: Lectures: TWRFM, 10-12p, January 19-26, room 45-102
This course explores the intersection of machine learning and human neural interfaces. How can ML techniques be used to decode and modulate neural activity in the human brain? Introduces students to the emerging field of brain foundation models. Topics include: basics of electrophysiology and neural recording, self-supervised learning for brain signals, neural decoding models, closed-loop stimulation design, and ethical considerations in brain-computer interfaces. Focuses heavily on direct electrical recording and stimulation of the human brain (using microelectrodes, EEG, sEEG, ECoG). Features guest talks by researchers and practitioners. Students will gain hands-on experience working with real human neural datasets and learn to build models that can interpret and potentially enhance human neural activity. Suitable for students with a background in machine learning. Assignments focus on analyzing neural data and culminate with a final project research proposal.
6.S915 Large Language Models from Scratch §
Level: U
Units: 3
Grading: P/F
Instructors: Daniel Li (dali@mit.edu)
Sponsor: Paul Pu Liang
Schedule: Lectures: Monday - Friday, 2:00–4:00pm, Jan 5–16, 2026, room 37-212 Recitation Sessions: Monday - Friday, 4:00–5:00pm, Jan 5–16, 2026, room 37-212 Optional Office Hours: Monday - Thursday, 5:00-6:00pm, Jan 5-15, 2026, room 26-314; Friday, January 16, 5-6pm, room 38-166.
Over these two weeks, students will explore key techniques for building and fine-tuning large language models (LLMs) from scratch, with a strong emphasis on hands-on coding. Classes include lectures followed by recitation sessions with exercises designed to ensure deep engagement with the material. Lectures and recitations cover technical details not typically addressed in MIT courses. Topics include working with encoded data, coding attention mechanisms, implementing GPT models from scratch, pretraining on unlabeled data, saving model structure, downloading GPT-2 weights, and fine-tuning for classification and instruction-following tasks. Linear algebra and other mathematics exercises will support understanding of the LLM foundations. This course emphasizes practical, hands-on experience over theory, preparing students for real-world applications and machine learning engineering interviews. Assignments: This course involves daily in-class assignments, which will include both conceptual problems and coding exercises. Students will work on assignments during recitation sessions and office hours with guidance from the instructor. No additional work outside of lecture/recitation times will be necessary.
6.S916 How to Win at Texas Hold ‘Em Poker §
Level: U
Units: 3
Grading: P/F
Instructors: Laurie Wang – laurieee@mit.edu, Instructor, Course 6 Nathan Chen – nathanlc@mit.edu, Instructor, Course 6 Alex He – alexh22@mit.edu, Instructor, Course 6
Sponsor: Zachary Abel
Schedule: Lectures: Tuesday/Thursday at 1:00-2:30 pm, 1/06 - 1/18 Workshop: Tuesday/Thursday at 2:30-3:30 pm,, 1/06 - 1/18 PokerNow Tournaments: Starting 1:00 pm on Jan 11, Jan 18, Jan 25, virtual Office Hours: Monday 3:00-5:00pm, starting 1/12
This course teaches the mathematical framework to construct strategies and make winning decisions in poker. Before taking this course, students should have an understanding of the rules of Texas Hold'em Poker and basic probability. Assignments include 3 graded psets, workshops, and tournaments.
6.S917 Tube and Early Transistor Circuits §
Level: U
Units: 3
Grading: P/F
Instructors: Joe Steinmeyer (jodalyst@mit.edu)
Schedule: Lectures: Wednesday Fridays, 2:30-4pm, January 7 - 30, room 32-155.
This class will study early electronics with a focus on vacuum tubes, early semiconductors, and other adjacent topics. While a largely technical class, we'll also look at some social aspects of these technologies. We will have lectures with accompanying readings, some technical and some more literary or in other disciplines. There will be lab exercises available to explore and build some circuits. Circuits will be kept below 30V for safety. Some familiarity with circuits and circuit theory(6.2000/6.002) is assumed, and if you're just starting out, I can try to help fill in some gaps, time-permitting. There are no homeworks/psets. There are no exams. The class is meant to be fun and low-pressure. IAP 25 Course site here (should be all viewable including syllabus): https://tubes.mit.edu/6S917/2025
6.S918 The Nexus of Games and AI (Nexus II) §
Level: U
Units: 3
Grading: P/F
Instructors: Nicole Hoffman (nicolemh@mit.edu) Fredo Durand , CSAIL, EECS Michael Stopa , Academic Manager, Sony Interactive Entertainment (SIE), course lecturer Ryan Valenza (Sr. Director, AI and Data Platform, SIE), course lecturer Erick Flores (SIE), course lecturer Gale Lucas (University of Southern California) , course lecturer Keri Carpenter (SIE), course lecturer Nate Gross (Sr. Manager, Software Engineering, SIE), course lecturer Ram Barankin (SIE), course lecturer Leanne Chukoskie (Northeastern University), course lecturer Jennifer Corbett (MIT), course lecturer Logan Olson (Haven and Nexus I alumnus), course lecturer
Sponsor: Fredo Durand
Schedule: Lectures: Mondays/Wednesdays/Fridays, 1-2p, January 5–16, 2026, room 32-144; (1/21 - 1/30, room 32-124) Student team projects presented on January 30. - Recording: Pending approval, lectures will be recorded and made available on MIT’s YouTube channel.
Computers and gaming have grown up together. Since Bertie the Brain learned to play Tic Tac Toe in 1950, computers have hosted, played, and designed increasingly sophisticated games as they have grown in power. For every computer science paper on the arXiv – from computer vision to LLMs to personal immersion to cognitive science and general AI – there are a half dozen use cases you can name in the creation of video games. Furthermore, video games provide new worlds and synthetic data that test and stretch the capabilities of machine learning models – so the relationship is synergistic. Through lectures and discussions, students in Nexus II will engage with topics including interactive storytelling, responsible AI, human–computer interaction, and advances in large-scale data platforms. Speakers from both academia and industry will share perspectives on how AI is reshaping the way games are created and experienced, and how games themselves are being used as testbeds for advancing AI. The course will combine a series of lectures and hackathon-style projects, the latter chosen from a list of examples or otherwise devised, to introduce students to game creation, current game-related research and an exploration of the technology, the art and the fun of video games. Audience: The course is targeted at those who have an interest in video games and machine learning. There are no specific prerequisites. Structure: Project-oriented course combined with lectures. Readings: Readings will be chosen from the literature. The program is designed to be accessible to a broad MIT audience, requiring no specialized background in computer science or game development. We encourage students with an interest in art, psychology, writing, social impact and design of games to participate.

IAP 2026 Activities

6.AYB: Automate Your Business §
Instructors: David Turturean, (davidct@mit.edu) Hassan Al Lail (MIT) Rafael Olivera-Cintron (MIT 2023)
Schedule: Lectures: Monday/Wednesday/Friday, 1-4pm, January 5-30, room 3-133 Labs in 38-501/601
Build dependable, real-world automations using LLMs and traditional software tooling. Students learn to analyze open-ended business problems, extract system requirements, and implement robust workflows combining APIs, databases, webhooks, and agentic tooling (e.g., MCP tools), with emphasis on determinism, error handling, testing, and human-in-the-loop design. Topics include systems thinking, context engineering, visual orchestration with n8n, RAG basics, multi-agent frameworks, and the business side of automation: sales, value pricing, and contracting. You'll ship working automations each week, culminating in a two-day hackathon and optional Agentic Arena competition. Learn the 20% that delivers 80% of automation.
CS Trivia §
Instructors: Selena Qiao, scq@mit.edu, Co-organizer, 6-3 Maggie Yao, yaohuili@mit.edu, Co-organizer, 6-3 Andrew Lee, andrewl2@mit.edu, Co-organizer, 6-3
Schedule: Lecture: January 28, 7-9 PM, room 4-231
Think you know your semaphores from your sockets? Come solo or with friends, test your computer science knowledge, and win a fun prize!
Elementary Category Theory and Univalent Foundations §
Instructors: Ananth Venkatesh (ananthv@mit.edu) Max Misterka Anthony Donegan
Schedule: - Lecture every day MTWRF 2-3 pm, room 34-302 - Recitation/office hours TR after class 3-4 pm, room 34-302
A survey of category theory through the lens of Haskell as a language of thought. We begin with the inception of category theory and the higher structures point of view and quickly ascend to modern categorical algebra, touching on homotopy type theory, topos theory, and applications. Some philosophical discussions, an introduction to concepts in algebraic topology, and applications in fields such as deep learning may be entertained. We expect mathematical maturity and a familiarity with proofs, but not knowledge of any particular branch of math. Assignments will likely consist of assigned readings and a few short problems related to material covered in lecture.
Future of AI: Foundation Models & Generative AI (CANCELLED) §
Instructors: Rickard Gabrielsson (brg@mit.edu)
AI breakthroughs like ChatGPT, Copilot, DALL-E, Stable Diffusion, AlphaFold, and self-driving cars raise the question: is AI finally living up to the hype? The key lies in foundation models and generative AI—technologies some claim have already reached AGI. This non-technical lecture series begins with a brief history of AI, explains the limits of supervised and reinforcement learning, and explores how self-supervised learning enables foundation models. We’ll discuss their impact across science and business. Open to all backgrounds.
IAP Quantum Winter School §
Instructors: Agi Villanyi, agivilla@mit.edu, co-instructor, EECS Cora Barrett, cb8@mit.edu, co-instructor, Physics
Schedule: Lectures: Tuesday - Friday, January 20-23, 9am -5pm, room 4-149.
Quantum computation is a growing field at the intersection of physics, computer science, electrical engineering, and applied math. This course provides an introduction to the basics of quantum computation. Specifically, we will cover some fundamental quantum mechanics, survey quantum circuits, and introduce the concept of quantum algorithms. This course is self-contained and does not require any prior knowledge of quantum mechanics. The course is a one-week bootcamp held from January 20-23 over IAP. The goal is to prepare students for the quantum hackathon hosted by iQuHack at the end of IAP. If interested, please apply here: https://docs.google.com/forms/d/e/1FAIpQLSeyGJmft0yyxPdjvTPtrDJuze9vlN8bBQ6rT3T5duQDPu-FQA/viewform. Applications due December 14th!
Introduction to Reinforcement Learning §
Instructors: Pipitchaya Sridam (sprite48@mit.edu) Co-instructor: Sarunyu Thongjarast, thong125@mit.edu, course 18
Schedule: Lectures: Monday - Thursday, January 5 - 15, 2-5pm, room 36-156; Friday, January 16, 2-5, room 32-141. Office Hours:
Provides a brief overview of the current principles underlying Reinforcement Learning through lectures and workshops. Students work together on real projects, in Python, to develop skills and understanding of reinforcement learning. The course concludes with a 1-week project sprint where students work in teams of 1-4 to develop a reinforcement learning model in a field of their choice and give a final presentation on the last day of class. Topics include Dynamic Programming, Markov Decision Process, Finite & Infinite Horizon, Bellman's Equation, Value Iteration and Q-Learning, Deep Q-learning, Model-free methods, Exploration vs Exploitation, and Multi-Agent Reinforcement Learning. The course is intended to be beginner-friendly. Experience in algorithm/programming is recommended but not required. Suggested Background: Programming (6.1010 or 6.1210), Probability (6.3700 or 18.600), Linear Algebra (18.06)
The Art of Magical Illusions §
Instructors: Alan Oppenheim (avo@mit.edu)
Schedule: Lectures: Tuesdays and Thursdays, January 6 - January 29, room 26-328
This course focuses on teaching and illustrating simple tricks and sleights with cards with an emphasis on performance. The goal is simple card tricks performed well. Each session will introduce and demonstrate some tricks and sleights and show how they work. Participants are expected to practice at least one of those between sessions and be prepared to perform for the group. Participants will also be encouraged to search out in books and on the web a new (simple in execution) trick to learn, teach and present to the class. It is intended for participants with no (or essentially no) serious prior experience with performing magic with cards or other props. Note: This offering is limited to 15 participants who have little or no experience with card tricks or performing magic. Interested participants should send an email to Al Oppenheim (avo@mit.edu) with a brief statement of why they’re interested in the course and what if any background they have in magic.
The Mechanical Watch §
Instructors: Gerald Jay Sussman (gjs@mit.edu) Master watchmaker Jack Kurdzionak (a friend and coauthor). Not MIT.
Schedule: Lecture: January 23, 11-1p, room 32-155 Lab on the following Saturday and Sunday.
This is a lecture for anyone in the MIT community, followed by a practicum for 32 MIT Students, where each student disassembles and reassembles an ETA 6497 movement.
Transcribing Prosodic Structure of Spoken Utterances with ToBI §
Instructors: Stefanie Shattuck-Hufnagel (sshuf@mit.edu) Dr. Alejna Brugos, co-instr. alejna99@gmail.com. Simmons College Dr. Nanette Veilleux, co-instr. nanette.veilleux@simmons.edu
Schedule: Lectures: Tues-Thurs 11 am-1 pm; online meetings
This activity presents a tutorial on the ToBI (Tones and Break Indices) system, for labelling certain aspects of prosody in Mainstream American English (MAE-ToBI). The course is appropriate for undergrad or grad students with background in linguistics (phonology or phonetics), cognitive psychology (psycholinguistics), speech acoustics or music, who wish to learn about the prosody of speech, i.e. the intonation, rhythm, grouping and prominence patterns of spoken utterances, prosodic differences that signal meaning and phonetic implementation.