Autonomous robotics contest emphasizing technical AI, vision, mapping and navigation from a robot-mounted camera. Few restrictions are placed on materials, sensors, and/or actuators enabling teams to build robots very creatively. Teams should have members with varying engineering, programming and mechanical backgrounds. Culminates with a robot competition at the end of IAP. Enrollment limited.

Battlecode is an AI programming competition in Java where students build virtual robots for real-time strategy gameplay. You'll enhance your programming skills, learn AI techniques, and collaborate with peers to create intelligent robots. The course concludes with a live Battlecode tournament. This year the competition will also have an experimental division in Python. Basic programming knowledge is recommended. Registration on subject website required. Battlecode Website: https://battlecode.org/

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.

Build autonomous poker players and acquire the knowledge of the game of poker ♦♣♥♠. Showcase decision making skills and apply concepts in mathematics, computer science and economics. Provides instruction in programming, game theory, probability, statistics and machine learning. Concludes with a final competition and prizes. Lunch provided.

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.

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. Complexed 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. Prerequisites: Understanding of basic circuit analysis provided in 6.200, 2.678, or equivalent. Prospective students who have not taken 6.200, 2.678, or an equivalent class will be required to pass a staff-created open-book pretest, prior to the start of IAP, that covers required circuit knowledge for the course. More information can be found at the course website: pcb.mit.edu

The rise of large language models has transformed software development and prototyping. Now, a single engineer can build and launch a full-scale app in hours or days. Mastering rapid prototyping is crucial, empowering students to become 10x developers. This course teaches AI-driven rapid prototyping, equipping students to design and ship apps quickly. You’ll gain hands-on experience building and launching AI-first web apps using the latest AI-driven dev tools. We cover full-stack essentials, from creating a simple next.js page to deploying a genAI model to the cloud. The course includes 6 lectures, 3 mini-projects, and a final project. Due to the limited availability, students need to apply through a form at iap.sundai.club.

This course provides a rigorous introduction to psychological theories in engineering terms, and provides hands-on machine learning practices. Designed for students interested in learning how psychological processes and social cognition can be modeled computationally, and how these models can be used as tools to better understand ourselves and others, and in the future, transform our understanding of human experience. This course is one of the first psychology courses, designed for engineers interested in understanding the human condition, while using machine learning as a tool to “navigate” the human mind. The covered topics include: applying machine learning to model complex cognitive processes of social cognition; using probabilistic programming and Bayesian machine learning to simulate and predict human behavior; using mechanistic computational models to predict and modulate brain signals in response to stimuli.

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, each with a corresponding recitation and problem set.

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.

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 and paper authorship.

Diffusion models are a class of generative models that iteratively refine noise into structured data. Although initially developed for image generation, they have been successful in many other domains such as robotics and molecular design. In this course we will introduce the basics of diffusion models and demonstrate how to build them from the ground up, culminating in a simple but powerful library to train diffusion models on custom data, as well as using state-of-the-art pretrained models for a variety of downstream tasks. This is an introductory course targeted at students and researchers who wish to learn about diffusion models and explore their applications to new domains, or those currently working with diffusion models and want to understand how to effectively modify and adapt them for their specific applications.

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 mathematical 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. For more information: https://diffusion.csail.mit.edu/

This course provides a comprehensive, hands-on introduction to training robots using state-of-the-art machine learning techniques. Key topics include data collection, machine learning methods such as Action Chunking Transformer (ACT) and/or Diffusion Policy, environment modeling in the MuJoCo simulator, and Real2Sim/Sim2Real techniques. Students will teleoperate a simulated robot in augmented reality via the Apple Vision Pro, and train a machine learning model to autonomously complete a task of their own design. The course culminates in a competition, judged on both robot performance and creativity of the chosen task. A solid working knowledge of Python and a basic understanding of machine learning are prerequisites. The course focuses entirely on the project, with no additional assignments.

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 algorithm 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.

Enrollment: Limited: Advance sign-up wit Prof. Leeb required Sign-up: by IAP Pre-Registration Deadline Attendance: Participants must attend all sessions every day, January 21,22, 23 9 AM to 5 PM Prereq: Short readings before each seminar day. A 3-day in-depth course focused on exploring the use of software tools, microcontrollers and communication concepts such as Bluetooth® Low Energy to create and implement Machine Learning projects. We will be using Infineon PSoC™ 6 development kits (provided by Infineon). The first two days will focus on lectures and instructor-led labs. The last day will consist of student teams creating a Machine Learning project. Infineon Imagimob Studio and ModusToolbox™ IDE and its features will be explored and explained. Students will receive in-depth instruction and will complete exercises related to: • Imagimob Studio and ModusToolbox™IDEs • Using the Bluetooth® LE and WiFi radios • The Infineon CY8CKIT-062S2-AIPSoC™Architecture and development environment • The kit contains multiple sensors on board such as RADAR, IMU, barometric air pressure, other sensors can be interfaced via the QWICC interface. Some programming experience is required. Experience with C programming is helpful but not required. PERMISSION OF INSTRUCTOR IS REQUIRED TO REGISTER. Email sbleeb@mit.edu for permission before registering. Registering for this course is a FIRM commitment to attend; others will be turned away to make room for you. Sponsor(s): Electrical Engineering and Computer Science Contact: Steven Leeb, sbleeb@mit.edu

This class will study vacuum tubes, early transistors, and other historically adjacent developments, as well as build some circuits using them. Labs will involve building a FM crystal receiver, a audio tube amplifier (that you can keep provided it passes safety inspection), and several germanium transistor circuits. There will be short psets on theory and practice. Prerequisite are be 6.2000 (6.002). Enrollment may be limited.

We live in an age of big machine learning models, where modern deep neural networks comprise hundreds of billions of parameters. As these models continue to scale, the ever-growing requirements on energy efficiency and computation speed have sparked a new industry in designing specialized computing hardware optimized for neural networks. In this constantly evolving landscape of technology, light-based computing, commonly referred to as "optical" or "photonic" computing, is a revolutionary paradigm shift promising higher computing frequency and less energy consumption than traditional digital computing. This course aims to introduce students to this exciting and rapidly growing field, focusing particularly on: 1. How can light be used for computing, and why should we build optical computing hardware? 2. What are the fundamental devices used for photonic computing? 3. What are the current and emerging research topics at the intersection of optics and AI hardware? This course will integrate lectures, lab tours, demos, and a final team presentation on new research areas in photonic computing.

This course covers the unique landscape of entrepreneurship within the field of artificial intelligence (AI). Students will learn how to start a company and how starting an AI company differs from traditional startups, focusing on innovative business models required in this rapidly evolving technological era. The course emphasizes a hands-on approach, utilizing case studies and biographical perspectives of successful AI entrepreneurs to understand what strategies have worked and what haven't.

In this workshop, we will discuss non-technical skills that are critical for a successful Ph.D. journey and professional career. Topics will focus on personal and interpersonal skills, including developing self-confidence, giving/receiving feedback, and managing conflict and stress. We will showcase how these skills can be used to address real scenarios/challenges encountered during the Ph.D. journey (e.g., managing feedback with your advisor or other students). In addition to providing resources and guidance and how to use these skills, the workshop will also contain a skills development component, where students will have an opportunity to practice the skill.

Quantum computation is a growing field at the intersection of physics, computer science, electrical engineering, and applied math. This intensive, hands-on program provides an introduction to the basics of quantum computation, using foundational concepts from quantum mechanics -- superposition, interference, and entanglement -- to guide the learning. Over the course of five days, students will master the fundamentals of quantum mechanics and linear algebra and learn how to construct their own quantum algorithms. Students will also learn how to code quantum circuits using quantum SDKs. This course is self-contained and does not require any prior knowledge of quantum mechanics. The primary goal of the program is to prepare students for the iQuHACK quantum hackathon hosted by iQuISE at the end of IAP. The first four days will consist of lectures in the morning followed by a problem solving session in the afternoon. The final day will consist of short presentations by students as well as talks from industry sponsors.

This course teaches students how to use interpretable machine learning and spatial visualization to analyze climate justice datasets and use insights to advocate for frontline communities, while focusing on data and algorithmic ethics in the context of climate. Students should have a technical background in classical machine learning and Python programming. There will be reflections to engage students in experiential learning as well as hands on coding assignments to apply concepts.

MIT Reality Hack is a week-long event where participants design and build immersive experiences in virtual and augmented reality (VR/AR). You'll collaborate in teams to create innovative VR/AR prototypes using industry-standard tools such as Unity, Unreal Engine, and XR hardware like motion trackers and haptic devices. Throughout the hackathon, you'll enhance your skills in real-time 3D development, spatial audio, and user experience design for immersive environments. In addition to hands-on prototyping, participants will attend technical workshops covering VR/AR development workflows, XR interaction paradigms, and relevant hardware technologies. Mentorship from experienced developers and industry professionals will support your learning and help bring your ideas to life. Entries for prizes judged on creativity, technical achievement, and real-world applicability. No formal prerequisites are required, but familiarity with programming concepts (e.g., Python, C#), 3D software, or basic Unity development is recommended. Participants from all skill levels are welcome. Registration on subject website required for acceptance. Reality Hack website: https://www.mitrealityhack.com/

Most watches these days are electronic miracles, but we cannot easily get insight into how they work. The traditional mechanical watch is different in that we can see all of the parts and how they interact. A mechanical watch is a high-precision mechanical device with lots of clever ideas and insights that we can learn from. Lecture Professor Sussman will explain the theory and design of the mechanical watch and its relationship to an electronic impulse-driven oscillator. There will be a discussion of friction (resistance) and its effect on Q and timing precision. He will explain why it is essential for the impulse to be supplied to the oscillator at the zero crossings of the angle, and why the oscillator will enter a limit cycle of a known amplitude. Practicum We will have a practicum (limited to 32 students) where each student will disassemble and reassemble an ETA 6497 movement. Besides observing the detailed construction of such a mechanism they will experience manual technique to safely manipulate very small objects with precision tweezers and screwdrivers, using magnification.

This course 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.

This course integrates the technical depth of Electrical Engineering and Computer Science with the entrepreneurial and funding aspects of AI startups. Aimed at EECS students, it focuses on how cutting-edge AI technologies influence venture funding decisions. Students will explore the technical due diligence process, learn to communicate complex technical ideas to investors, and understand the scalability and implementation challenges of AI solutions from a funding perspective. The course includes lectures, technical case studies, guest speakers from tech-focused venture capital firms, and practical assignments that emphasize the intersection of technology and funding in the AI startup ecosystem. Topics Covered: Technical Due Diligence in Venture Funding: Understanding what investors look for in AI technologies. Communicating Technical Concepts to Investors: Techniques for explaining complex ideas to non-technical audiences. Scalability and Performance of AI Systems: Technical challenges in scaling AI solutions and their impact on funding. Intellectual Property in EECS Innovations: Patent strategies, open-source considerations, and proprietary technologies. Regulatory and Ethical Considerations: How technical compliance affects venture funding. AI Hardware Acceleration: Impact of specialized hardware on AI startup viability. Cybersecurity in AI Applications: Security considerations for AI systems and their importance to investors. Emerging Trends in AI Technology: Staying ahead of technological advancements that attract venture capital. Case Studies of Technical Successes and Failures: Lessons learned from previous AI startups. Types of Assignments: Technical Pitch Presentation: Develop and present a pitch that highlights the technical innovation of an AI startup. Due Diligence Report: Create a mock technical due diligence report for an AI technology or startup. Scalability Project: Analyze and propose solutions for scaling an AI system, considering technical limitations and funding implications. Intellectual Property Analysis: Evaluate IP strategies for a given AI technology, including patent searches and infringement risks. Guest Speaker Reflections: Write summaries and analyses of insights gained from industry experts in technical venture funding.