IAP 2023 – MIT EECS

Here are the course offerings available through EECS during IAP 2023 for credit (non-credit activities are further down).

6.9300/6.9302 StartMIT: Workshop for Entrepreneurs and Innovators

Level: U/G
Units: 4-0-2
Pass/Fail
Instructors: Susan Neal (sneal@mit.edu); Daniela Ruiz Massieu (druizm@mit.edu)
Schedule: Monday-Friday, 8-6pm, January 3 – January 19, room 1-190

Description

Designed for students who are interested in entrepreneurship and want to explore the potential commercialization of their research project. Introduces practices for building a successful company, such as idea creation and validation, defining a value proposition, building a team, marketing, customer traction, and possible funding models. Students taking graduate version complete different assignments.

Students should apply at https://orbit.mit.edu/applications/startmit23

6.9600 Mobile Autonomous Systems Laboratory: MASLAB

Level: U
Units: 2-2-2
Prereqs: none
Pass/Fail
Instructor: John Zhang (johnz@mit.edu)
Faculty Advisor: Professor Russ Tedrake (russt@mit.edu)
Schedule: Monday-Friday, 10a-12, January 9-13, room TBD

Description

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.

6.9610 The Battlecode Programming Competition

Level: U
Units: 2-0-4
Prereqs: none
Pass/Fail
Instructor: Andy Wang (wangandy@mit.edu) or battlecode@mit.edu
Faculty Advisor: Professor Pulkit Agrawal (pulkitag@mit.edu)
Schedule: Monday – Friday, 7-10pm, January 9 – January 20, room 32-155

Description

Artificial Intelligence programming contest in Java. Student teams program virtual robots to play Battlecode, a real-time strategy game. Competition culminates in a live BattleCode tournament. Assumes basic knowledge of programming.

6.9620 Web Lab: A Programming Class and Competition

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: none
Instructor: Nicholas Tsao (weblab-staff@mit.edu)
Faculty Advisor: Professor Arvind Satyanarayan (arvindsatya@mit.edu)
Schedule: Monday-Friday, 11-3, January 9 – February 3, room 26-100

Description

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. Topics include version control, HTML, CSS, JavaScript, ReactJS, and nodejs. All teams are eligible to enter a competition where sites 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.

6.9630 Pokerbots Competition

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: none
Instructor: Matt McManus, (mattmcm@mit.edu)
Faculty Advisor: Silvina Hanono-Wachman (silvina@mit.edu)
Schedule: MWF, 10-11:30am, January 23 – January 27, Room 6-120

Description

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. Concludes with a final competition and prizes. Enrollment limited.

6.S086 Transcribing Prosodic Structure of Spoken Utterances with ToBI

Level: U
Units: 1-0-5
Prereqs: linguistics, acoustic or psycho linguistics or speech science background suggested
Pass/Fail
Instructor: Dr. Stefanie Shattuck-Hufnagel (sshuf@mit.edu), Drs. Alenja Brugos and Nanette Veilleux, Simmons University, Boston
Schedule: Tuesdays & Thursdays, 11-1, January 10 – February 2, room 36-112

Sign up using this form https://forms.gle/X879LkKMMGacR1726 in advance by January 7th, and preregister on WebSIS, listeners accepted.

Description:

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.

Contact Stefanie Shattuck-Hufnagel, sshuf@mit.edu.

6.S088 Modern Machine Learning: Simple Methods that Work

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: see below
Instructor: Adityanarayanan Radhakrishnan (aradha@mit.edu)
Faculty Advisor: Professor Caroline Uhler (cuhler@mit.edu)
Schedule: MWF, 1-2:30, January 9 – February 3, room 32-141

Description

Over the past decade, interest in machine learning research has spiked drastically, with advancements in deep learning being a significant driving force. Indeed, deep learning has transformed many areas in computer science including computer vision, natural language processing, and reinforcement learning. Unfortunately, given the rapid pace of progress in deep learning, a newcomer looking for a simple set of guiding principles for building machine learning applications can be easily overwhelmed by the nuances of training deep networks. Thus, motivated by recent developments in machine learning, we present a simple class of machine learning methods that are easy to implement and which achieve competitive performance in practice. In particular, our methods rely on the recently established equivalence between kernel regression and infinite width neural networks given by the neural tangent kernel (NTK). In addition to being a theoretical tool for understanding neural networks, we demonstrate that the NTK is a simple method for achieving competitive results in a variety of machine learning applications including regression, classification, image completion, and drug screening. We provide problem sets containing both theoretical and coding exercises with the aim of (1) providing newcomers, a simple toolkit for building effective machine learning models in practice and (2) preparing interested students for research in the area.

Recommended Pre-requisites

Knowledge of linear algebra (level of 18.06 or 18.700) and probability (level of 6.041 or 18.600) is generally assumed. Familiarity with Python (in particular, NumPy) is also assumed. While not necessary for the course, knowledge of Fourier analysis (18.103), functional analysis (18.102), random matrix theory (18.338), and complex analysis (18.112) is suggested for students who want to pursue research in this area.

6.S089 Introduction to Quantum Computing

Level: U
Units: 2-0-4
Pass/Fail
Prereqs:
Instructors: Amir Karamlou, (karamlou@mit.edu) and Agnes Villanyi (agivilla@mit.edu)
Faculty Advisor: Professor William Oliver
Schedule: MWF, 3-5pm, January 9 – February 3, room 32-155

Description

Quantum computation is a growing field at the intersection of physics, computer science, electrical engineering and applied math. This course introduces 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.

6.S090 nanoStories – Workshop on Science Communication at the Nanoscale

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: none
Instructors: Professor Vladimir Bulovic (bulovic@mit.edu) and Dr. Annie I. Wang (aiwang@mit.edu)
Schedule: Tuesdays, Thursdays, Fridays, 2-4pm, January 10 – January 20, room 12-3005 also hybrid option

Description

Designed for students with an interest in science communication and STEAM outreach. Guided by instructors, in each two-hour class students will explore a new topic, jointly developing an instructional narrative to be told in text, video, and/or interactive multimedia. Outside of MIT labs, nanoscience and nanotechnology appear mysterious. Help us demystify them! The content of the classes will reflect research/exploratory interests of participants.

6.S091 Causality: Policy Evaluation, Structure Learning, and Representation Learning

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: see below
Instructor: Chandler Squires (csquires@mit.edu)
Faculty Advisor: Professor Caroline Uhler (cuhler@mit.edu)
Schedule: Tuesdays & Thursdays, 1-3pm, January 10 – February 2, room 4-231

Description

Covers introductory material from three active research areas related to causality and machine learning. In the first third of the course, we will discuss the fundamentals of policy evaluation, where a known causal structure is used to estimate causal quantities such as (conditional) average treatment effects. In this section, we will cover algorithms for identification of causal estimands, as well the principles behind state-of-the-art estimation methods based on double/de-biased machine learning. In the second third of the course, we will consider causal structure learning, i.e., the estimation of an unknown causal structure from data. We will cover classical algorithms such as the PC algorithm, as well as newer methods which incorporate interventional data and allow for unobserved confounding. We will also cover experimental design techniques for causal structure learning. In the final third of the course, we will discuss the emerging field of causal representation learning, highlighting recent papers which connect machine learning with more traditional causal principles.

Recommended Prerequisites: Knowledge of probability (level of 6.3700) and statistics (level of 18.650) is generally assumed. Familiarity with Python is also assumed.

6.S092 The Art and Science of PCB Design

Level: U
Units: 1-0-5
Pass/Fail
Prereqs:
Instructor: Fischer Moseley, (fischerm@mit.edu) Aditya Mehrotra (adim@mit.edu)
Faculty Advisor: Joe Steinmeyer (jodalyst@mit.edu)
Schedule: MWF, 1-2:30, January 9 – February 3, room 34-101

Description

This class is project-focused, and you will build a PCB of your own design in this class. This includes schematic capture, board layout, assembly, and debugging to get it working. We’ll pay for your PCBs and the components on them – there’s no cost to you. We’ll do this by teaching you each step of the process in lecture, letting you try your hand at it, and then following up on your work with a design review (DR). DRs are 20-minute chunks where we sit with you and go over your design, and provide feedback before moving onto the next step. You’ll have three of these – one after you make your schematic, one after you make your board layout, and one before you send them to the fab house for fabrication. This class is open to all skill levels, and you don’t need previous electrical design experience! We’ve got a few different ways to progress through the class, and your background will probably dictate which one you choose.

6.S094 Introduction to Quantum Network

Level: U
Units: 2-0-4
Pass/Fail
Prereqs:
Instructor: Yuqin Sophia Duan (sophiayd@mit.edu)
Faculty Advisor: Professor Dirk Englund (englund@mit.edu)
Schedule: MWTh, 3-5, January 9 – January 19, room 34-141

Description

After 20th century, Quantum innovations has brought us to a new dimension of information. Quantum network plays an important role for the physical implementation of quantum computing, quantum communication and sensing. In this course, aims to give you a concrete glance of state-of-art quantum technologies, covering the fundamental concepts of quantum network protocols, measurements, device physics and using analytical simulation tools, with guest lectures from frontier researchers covering different topics.

6.S095 Intermediate Probability Problem Solving

Level: U
Units: 2-0-4
Pass/Fail
Prereqs: Calculus (18.01). Prior coursework in probability is not required but is helpful.
Instructors: Peggy Yang (peggyy@mit.edu), Richard Chen (rachen@mit.edu), Peter Hoffman (hoffmanp@mit.edu), Zi Song Yeoh (zscoder@mit.edu)
Faculty Advisor: Professor Guy Bresler (guy@mit.edu)
Schedule: Lectures Tuesdays & Thursdays, 7-10pm, room E25-111, Recitations WF2, rooms 32-124 and 35-225, January 9 – February 3

Description

6.S095 is a survey of advanced problem solving techniques in probability, random variables, and stochastic processes. It picks off 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 6.046, 6.262, 6.265, 6.437, 6.438, and 6.856.

~~6.S096 Introduction to the C Programming Language~~ CANCELLED.

6.S097 Ultrafast Photonics

Level: U
Units: 1-0-5
Pass/Fail
Prereqs:
Instructor: Donnie Keathley, Principal Research Scientist, RLE, (pdkeat2@mit.edu), Prof. William Putnam, UC Davis
Schedule: Tuesdays & Thursdays, 11-12:30, January 10 – February 2, room 34-304

Description

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. Note that 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.

6.S098 Introduction to Applied Convex Optimization

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: multivariable calculus (18.02), linear algebra (18.06 or 18.061), basic probability, programming, mathematical maturity (e.g., 6.042)
Instructor: Theo Diamandis (tdiamand@mit.edu)
Faculty Advisor: Professor Pablo Parrilo (parrilo@mit.edu)
Schedule: Tuesdays & Thursdays, 1-2:30, room 32-124

More information can be found at convex@csail.mit.edu

Description

Convex optimization problems appear in a huge number of applications and can be solved very efficiently, even for problems with millions of variables. However, recognizing what can be transformed into a convex optimization problem can be challenging. This course will teach you how to recognize, formulate, and solve these problems. We will briefly survey theoretical results in convex analysis, but the majority of the course will focus on formulating and solving problems that come up in practice. Applications will include signal processing, statistics & machine learning, finance, circuit design, mechanical structure design, control, power systems, and other areas based on student interest. This course is designed for advanced undergraduates and beginning graduate students.

6.S099 Machine Learning Single-Cell Cancer Immunotherapy Competition

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: see below
Instructor: Professor Caroline Uhler (uhler@mit.edu)
Schedule: Tuesdays & Thursdays, 11:30-1, January 10-February 2, room 26-168

Description

Machine learning contest to predict the effect of genetic perturbations on T cells in the context of cancer. The goal is to identify genetic perturbations that make T cells more effective at killing cancer cells. All participants enter a competition with prizes. The top scoring submissions based on the prediction tasks will be validated experimentally. Assumes basic knowledge of programming, but no biological background.

Recommended prereqs:

We expect students to be familiar with at least one programming language (e.g. Python) at the
level of 6.1010. In addition, it is recommended that students have taken a course in data
science and/or machine learning such as 6.3720 (Introduction to Statistical Data Analysis),
6.3900 (Introduction to Machine learning) or 6.3730/IDS.012 (Statistics, Computation and
Applications). No background in biology is required for this course.

6.S187 Code for Good

Level: U
Units: 1-0-5
Pass/Fail
Prereqs:
Instructor: Tarang Lunawat (tarang@mit.edu) or Contact: codeforgood@mit.edu
Faculty Advisor: Professor Frans Kaashoek (kaashoek@mit.edu)
Schedule: Tuesdays & Fridays, 3-5, January 10 – February 2, room 32-124

Apply at http://codeforgood.mit.edu/apply

Description

For this class, students have the opportunity to work on software-related projects with local nonprofit organizations. Teams of 3-4 students choose a project that is of interest to the group, or suggest their own project ideas. 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.

Project listings and detailed information are available on the website: http://codeforgood.mit.edu/programs/iap-class/

6.S190 Concepts in Embedded Machine Learning

Level: U
Units: 3 units
Pass/Fail
Prereqs: Permission of Instructor (see below)
Instructors: Professor Steven Leeb (sbleeb@mit.edu), Greg Landry, Ali Atti, and Patrick Kane (Infineon)
Schedule: Tuesday, Wednesday, Thursday, 9-5pm, January 17-19, room, 34-501

Enrollment Limited to 30: Advance sign up required

Description

A 3-day in-depth course focused on exploring ML concepts such as voice and gesture recognition. 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 ML projects. Infineon ModusToolbox™ IDE and its features will be explored and explained. Students will receive in-depth instruction and will complete exercises related to:

Some programming experience is required. Experience with C programming is helpful but not required.

Voice Recognition
Gesture recognition
The Infineon CY8CKIT-062S2-43012 Architecture and development environment
The CY8CKIT-028 TFT shield (TFT, audio, and multiple sensors)
ModusToolbox IDE

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.

6.S197 Tube Electronics

Level: U
Units: 1-0-5
Pass/Fail
Prereqs: 6.2000 (6.002)
Instructor: Joseph Steinmeyer (jodalyst@mit.edu) , Senior Lecturer, EECS
Schedule: Tuesdays & Thursdays, 2:30-4, January 10 – February 2, room 34-304

Description

This class will study vacuum tubes 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).

IAP 2023 Activities

Here are the activities available sponsored by EECS during IAP 2023.

Introduction to the D Programming Language

Instructor: Michael Coulombe (mcoulombe@mit.edu)

Schedule: Thursday, January 26, 3-5pm, room 32-141

Description

Join this interactive session on D to learn the basics and unique features behind this powerful and expressive general-purpose programming language. You will also hear from two active members of the D community to share their experience using D in industry, academia, and beyond. Open to new and advanced programmers, but some background in another language (such as Python, C/C++, Java, etc…) will be expected. Check out the D homepage to learn more in advance at https://dlang.org/

Bring your laptop to participate in the live coding!

Guest Speakers include:

Steven Schveighoffer started using D in 2007. He is one of the core developers of the standard library, having written the array runtime, and various other pieces, mainly focusing on memory safety and performance. He has been working in software for over 24 years, most recently as a software consultant for several clients using D. He maintains several D projects, including mysql-native (a fully-D msyql client), raylib-d (D bindings for raylib), and iopipe (a high performace i/o library).

Mike Shah is currently an Associate Teaching Professor at Northeastern University in the Khoury College of Computer Sciences. Mike’s primary teaching interests are in computer systems, computer graphics, and software engineering. Mike’s research experience is in the areas related to performance engineering (dynamic analysis), software visualization, and computer graphics. Along with teaching and research work, Mike has occasionally done consulting work as a 3D Senior Graphics Engineer using OpenGL. More recently, Mike has been working on personal and professional projects in the DLang, and spending time building training materials available on YouTube.

High-performance computing basics

Instructors: Guillaume Leclerc (leclerc@mit.edu) EECS Grad Student and Djuna von Maydell (djuna@mit.edu) BCS Grad Student

Schedule: Monday-Friday, January 9-13, 2-4pm, room 36-112

Description

Most research projects involve auxiliary computing tasks: preparing / processing data, solving optimization problems etc… A popular approach is to focus on simply getting code that works. In this class we introduce a panel of simple strategies to improve the performance of research code by orders of magnitude with minimal additional work, enabling you to solve bigger problems. The class will use Python (and sometimes Julia) for illustration but the takeaways will be programming language agnostic.\

Noise, Perception and Learning: Applications in AI art

Instructor: Sarah Muschinske (muschins@mit.edu) EECS Grad Student, Aspen Hopkins, PhD student in EECS, CSAIL; Mikey Fernandez, PhD student in MechE, Media Lab; Logan Engstrom, PhD student in EECS, CSAIL; Andrew Ilyas, PhD student in EECS, CSAIL; Chandler Squires, PhD student in EECS, LIDS; John Simonaitis, PhD student in EECS, RLE

Schedule: Tuesdays & Thursdays, January 10 – February 2; Wednesday, January 25; 5-7pm, room 36-112

more information can be found at: https://sarahmuschinske.github.io/gen_art_mit/

Description

This seminar-style course will cover fundamental physical, chemical and biological origins of noise & how it affects perception for both natural and artificial neural networks. This will be applied to generation of AI art.

Future of AI: Self-Supervised Learning and World Models

Instructor: Rickard Brüel Gabrielsson (brg@mit.edu) EECS Grad Student

Schedule: Thursdays January 12 – February 2, 2-3pm, room 24-121

Description

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 self-supervised learning and it is changing everything. With the help of it, Facebook’s Yann LeCun now believes he sees a way to Artificial General Intelligence (AGI) in the form of foundation models. In this non-technical series of lectures, we will start with the history of AI, then with what supervised learning and reinforcement learning is missing, and conclude with the deep practical and foundational implications of self-supervised learning. We cover applications in both science and business. Lectures (Thursdays at 2-3pm, room 24-121) will be recorded and all backgrounds are welcome. Website at https://futureofai.mit.edu/

Building skills for a successful PhD

Instructor: David Nino (dnino@mit.edu), Senior Program Manager and Senior Lecturer Graduate Program in Engineering Leadership, and Prof. Vivienne Sze, EECS (sze@mit.edu)

Schedule: Tuesday, January 31, 1-4pm, room 32-144

Please note that you need to register in advance using this form. Attendance will cap at 50 participants.

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 and stress. 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.