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. Basic programming knowledge is recommended.

Students form teams of up to three members and learn to build functional and user-friendly websites. Lectures and workshops teach everything you need to build a modern web application. Topics include Git, HTML, CSS, JavaScript, React.js, Node.js, MongoDB, WebSockets, building with AI chatbots, and more! 🌱 All teams are eligible to enter a competition where sites will be judged by industry experts, with over $10,000 in prizes. Both beginners and experienced web programmers are 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.

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!

This course provides an introduction and hands-on practices to the applications of machine learning in molecular design and engineering. The covered topics include: analyzing molecular properties using data-driven methods, using generative modeling, and applying combinatorial optimization approaches to design novel functional molecules such as new drugs. Unlike 6.C51+10.C51, this class adopts a practical, bootcamp-style format that focuses on designing novel functional molecules. We don’t only introduce new concepts but immediately demonstrate their applications with code, which is complemented by in-class coding exercises. The course includes a competition-based project that simulates real-world molecular discovery scenarios. In the final week of the course, we will host notable guest lecturers who will introduce students to cutting-edge research topics.

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 their phonetic implementation.

ChatGPT, Code Pilot, CLIP, Dall-E, Stable-Diffusion, AlphaFold, Self-driving cars – is now the time that AI lives up to all its hype? What's the secret sauce behind these recent breakthroughs within AI? It’s called foundation models and generative AI, and it is changing everything. With the help of it, some believe that Artificial General Intelligence (AGI) has already been achieved. In this non-technical series of lectures, we will start a short history of AI, then with what supervised learning and reinforcement learning is missing, and conclude with the deep practical and foundational implications foundation models. We cover applications in both science and business. All backgrounds are welcome. For more information check https://www.futureofai.mit.edu/

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 most significant quantum algorithms. Furthermore, we will survey advanced topics towards the end of the course. In the past, these topics have included quantum error correction, quantum communication, and applications to fields ranging from machine learning to chemistry. This course is self-contained and does not require any prior knowledge of quantum mechanics.

The course will give a graduate or advanced undergraduate-level introduction to the fields of causal inference, causal structure learning, and causal representation learning. Students will need background in probability, statistics, and linear algebra. They will complete assignments consisting of a mixture of theoretical problems and coding exercises.

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.

Many applications in cryptography and theoretical CS require some number theoretic background, which is often not taught in introductory classes. In this class, we will teach those concepts. We begin with a quick review of the basics: Euclidean algorithm and modular arithmetic. We will then cover multiplicative modular arithmetic (Euler, Fermat), elements of group theory, the discrete logarithm problem, and elementary analytic number theory if time permits. There will be a focus on computational methods as well, using Sage and other Computer Algebra Systems. The assessments will consist of weekly homeworks with a small final project. The expectation is that students have taken 6.042/6.1200 or a class at a similar level in discrete mathematics, as well as some coding experience.

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. This course is designed to overlap and coordinate with an ultrafast photonics course taught by Prof. William Putnam at U.C. Davis. Dr. Keathley will lead the lectures and course at MIT, with online material, such as lecture recordings and notes, being shared between MIT and UC Davis.

Statistical hypothesis testing is used in many fields to evaluate whether a result is statistically significant. This course will cover how to formulate a statistical hypothesis and draw conclusions from a test procedure. Starting with t-tests--the primary workhorse of hypothesis testing, students will then become well-versed in common approaches to test for differences in means, goodness-of-fit, independence, equality of distributions, and significance in linear regressions. Two essential topics students will also learn are correcting for multiple hypotheses and power to detect an alternative hypothesis. This course will cover roughly the same concepts as the 6 lectures in the hypothesis testing unit of 6.3720. The IAP class will be taught at a more introductory level in a hands-on style with a lecture and a problem-solving component at each class session. Problem sets will include theoretical and computational exercises. The objective of this course is for students to gain a clear understanding of when to use hypothesis tests, how to select a test and perform it correctly, and how to avoid common pitfalls when interpreting the results.

For this class, students have the opportunity to work on software-related projects with local nonprofit organizations. Teams of 4-5 students are assigned a non-profit that is of interest to the group, and work on the non-profit-proposed project for the duration of the term. Students are mentored by a representative from the nonprofit organization as well as subject instructors. During the entirety of the course, students have access to mentors and other resources. At the conclusion of the course, students will deliver their project to their nonprofit organization, and they’ll also have the opportunity to show off their projects at an exposition that is open to representatives of the nonprofit organizations, mentors, and the general MIT community.

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. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors.

BLE, CAPSENSE™ , and Classic Video Games Steven B. Leeb (EECS), Greg Landry,Ryan Morse, Ali Atti, and Patrick Kane (Infineon) Enrollment: Limited: Advance sign-up required Sign-up: by IAP Pre-Registration Deadline: January 8, 2024 Attendance: Participants must attend all sessions every day, Limited to 30 participants Prereq: Short readings before each seminar day. A 3-day in-depth course focused on exploring communication concepts such as Bluetooth Low Energy (BLE) and CAPSENSE™. We will be using Infineon PSoC 6 development kits and shields (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 video game project. InfineonModusToolbox™ IDEand its features will be explored and explained. Students will receive in-depth instruction and will complete exercises related to: BLE, CAPSENSE, and communicating to TFT and other sensors. The Infineon CY8CKIT-062S2-43012Architecture and development environment The CY8CKIT-028 TFT shield (TFT, audio, and multiple sensors) ModusToolboxIDE 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

Applications such as quantum simulation and quantum computation require exquisite control of microscopic quantum systems such as ultracold atoms, ions or colour centres. This control can be achieved with beams of light in the visible wavelength. These beams are directed at the atomic system where they excite the quantum state. We want to discuss how these control beams are generated using miniaturised optical circuitry in state of the art integrated photonic chips and then delve into applications in quantum computation and simulation. The course will comprise tutorials from researchers working in photonics and atomics as well as hands-on experience with state of the art technology. The participants will actively engage to build experiments and control photonic chips as well as model quantum circuits.

This course aims to provide a brief introduction to the rapidly evolving field of computational pathology, with an emphasis on modern deep learning-based algorithms and tools for analyzing pathology images (both regions of interest and gigapixel images). Topics include supervised, unsupervised, and self-supervised machine learning and their application to core image analysis tasks (classification, regression, segmentation, etc.) in the context of histopathology. Emerging trends such as visual language representation learning and foundational models and parallel fields such as multimodal image / omics analysis will also be discussed if time permits. Labs will focus on gaining hands-on experience with representative algorithms in computational pathology using Python and the Pytorch deep learning framework. Basic familiarity with linear algebra, python programming and machine learning is recommended but the course material will be self-contained.

Poker, a game of strategy, investment, and statistics, is highly relevant in today’s society because of the skills it develops in money management, finance, and decision making in general. This course teaches mathematical strategies and the concept of balance used to win at poker. Lectures will focus on presenting key concepts in poker, as well as walking through relevant examples pertaining to the concept. Workshops will focus on the practical application of these concepts in-game. The general format of these workshops will be a (play-money) cash game setting in-person either with physical cards and chips or on PokerNow, depending on attendance and capacity restrictions. A TA will be present at each table leading discussion and hand analysis. We may add additional workshop options depending on student/instructor availability. PokerNow tournaments will be held weekly throughout the class. There will be prizes for each tournament, provided by the MIT Poker Club. Students should have knowledge of: Rules of Texas Hold’em Poker (we will be doing a review during the first class), Basic Probability Please email 15.s50-instructors@mit.edu if you have any questions.

Might change this a bit: This class will study vacuum tubes and early transistors and build some simple circuits using them. We will focus on using a subset of tubes developed in the late 1950’s for 12V car circuits and this will keep us at a safe voltage relative to other tube circuit voltages. We’ll have a series of lectures and we’ll build towards a functioning AM regenerative receiver with some lab sessions, While the class will study tubes, it will also be a very basic and simple introduction to RF. Prerequisite will be 6.2000 (6.002).

Description: 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: How can light be used for computing, and why should we build optical computing hardware? What are the fundamental devices used for photonic computing? How do these photonic devices integrate into modern computer systems for real-world computing workloads? This course will integrate lectures, lab tours, demos, and a final team presentation on new research areas in photonic computing. Intended Learning Objectives: Upon completion of this course, students will be able to: 1) Understand the fundamental principles behind photonic computing. 2) Distinguish various optical devices, components, and systems crucial to light-based computing. 3) identify technical challenges in state-of-the-art photonic computing systems 4) design building blocks for future photonic computing systems Background required: Both undergraduate and graduate students are welcome. We aim to make this course accessible to a broad range of backgrounds. However, students should have taken at least one electromagnetism course at the advanced undergraduate level and one course in computer architecture and systems (e.g., combinational and pipelined arithmetic-logic units (ALU), in-order pipelined microarchitecture, etc.). Some examples of MIT courses that would be adequate background (old course numbers in brackets) are: Electromagnetism: 6.2300 [6.013] Electromagnetic Waves and Applications 6.S046/6.S976 Silicon Photonics 6.6330/6.6331 [6.602/6.621] Fundamentals of Photonics 6.6300 [6.630] Electromagnetic Waves Computer Architecture: 6.1910 [6.004] Computation Structures 6.5900 [6.823] Computer System Architecture Students should also be familiar with the basic principles underlying deep neural networks. Courses that cover this include 6.3900 [6.036] at the undergraduate level and 6.7900 [6.867] at the graduate level. Other courses may also be sufficient background–if you have questions about the prerequisites, please feel free to email the instructors. More details about the course syllabus can be found here: https://docs.google.com/document/d/1cydpEc-JpoMT9lEzVzf_iXrcXsGbWnFB3lJs7jOu-Zs/edit

A practically-oriented course on AI tools, ranging from web-based applications such as ChatGPT to developer platforms such as LangChain. The course is designed for a broad audience. It is not for credit and will not have assignments, though students will be given prompts for ways they might want to explore the tools on their own.

In this workshop, we will discuss non-technical skills that are critical for a successful PhD journey and professional career. Topics will focus on personal and interpersonal skills, including developing self-confidence, giving/receiving feedback, and managing conflict. We will showcase how these skills can be used to address real scenarios/challenges encountered during the PhD journey (e.g., managing relationships 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. Please note that you need to register in advance at https://forms.gle/DohC6psdLZRQ8qFAA (Attendance will cap at 50 participants)

In this course, I aim to provide a high-level view on various signal processing techniques, with an emphasis on practical implementation. The course will consist of a series of lectures, where we’ll discuss signal processing theory, followed by labs, where we’ll build and implement effects for a real-time digital audio effects processor. We’ll cover topics including sampling, interpolation, decimation, analog and digital filtering techniques, basic nonlinear signal-processing operations, and firmware optimization, among other more advanced concepts. Basic familiarity with Fourier analysis and microcontroller programming is strongly encouraged. Enrollment is limited, but you should sign up, especially if you play an electronic instrument! Sign up here: https://forms.gle/ELiYrT4pTTRwWmdb9

An introduction to AI safety, with a focus on potential catastrophic risks from AI systems that are more capable then humans. Topics include reward misspecification, goal misgeneralization, reinforcement learning from human feedback, and neural network interpretability. Includes weekly labs with programming exercises. No assignments. Familiarity with the basics of machine learning is assumed.

Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline. This is the first-ever course on DCAI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications. Register at https://dcai.csail.mit.edu/join

This tutorial gives a mathematical overview of decentralized exchanges, one of the core primitives in the blockchain-based financial ecosystem (i.e., decentralized finance or 'DeFi'). We will concentrate on the most common exchange design: the constant-function market maker. These markets have facilitated trillions of dollars of trading volume in the past two years. We’ll examine these exchanges using convex analysis and conic duality, allowing us to easily prove a number of interesting properties and draw connections to prediction markets. Much of the content is based on the preprint The Geometry of Constant Function Market Makers (https://arxiv.org/abs/2308.08066)

Visual design is a crucial element in various forms of scientific communication, ranging from papers, slides, to even videos. Nice figures serve as an effective tool in conveying complex ideas and information and makes them easily digestible and engaging; carefully designed tables can break down complex results and help people compare, contrast, and draw conclusions. While there is an increasing need for researchers to produce high-quality visuals, it remains to be a time-consuming and sometimes very challenging task. Despite the significant role they play, there is a noticeable lack of formal education dedicated to this aspect: imagine as if you were required to write a formal article without ever having been properly taught the principles of grammar. This subject aims to cover several key topics about visual designs in scholarly communication. It will be a combination of the fundamentals, skills, and case studies on scientific visual communications. The subject will be taught in a workshop format, with both lecture and lab sessions to help students grasp the key concepts as well as practice and put the knowledge into practice. We provide more details in the proposed activities and timeline. Drawing inspiration from previous IAP classes like “The Missing Semester of Your CS Education”, this class aims to cover topics that are not taught as part of the computer science curriculum. We intend to tailor the content to an audience with mostly computer science backgrounds, but we envision the class can be generally helpful for students and researchers from other departments.