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# IAP 2021 For-Credit Subjects:

## See Course 6 Non-Credit Activities

### 6.147     The Battlecode Programming Competition

Jerry W. Mao

Meets Mon-Fri, Jan 4th-15th, 7:00pm-9:00pm, Virtual

Final competition, 1/30 7pm Virtual

Register on WebSIS and our website. Attendance optional (lectures and tournaments are recorded). Listeners welcome.

Prereq: 6.0001

Level: U 6 units Graded P/D/F Can be repeated for credit

Artificial Intelligence programming contest in Java. Student teams program virtual robots to play Battlecode, a real-time strategy game. Assumes basic knowledge of programming, but not Java—lectures are held for the first two weeks to teach people the basics of Java and how to make your robots do intelligent things. Two teams of virtual robots, controlled by your code, roam the map gathering the resources necessary to build your army and win. Contestants learn to use artificial intelligence heuristics, pathfinding, and distributed algorithms. Competition culminates in a live Battlecode tournament.

Battlecode is a great opportunity to have fun and rapidly develop important software skills such as building a codebase from scratch, managing a large software system, and getting hands-on Java experience. For beginners, our lecture series (with dinner) walks you through creating your first bots and teaches more advanced techniques, and the Newbie Tournament—for first-time participants only—has its own share of the $30,000 prize pool. Compete in teams of one to four students. First-year students are encouraged to participate. Learn more at battlecode.org. Lectures are optional but helpful. Knowledge of real-time strategy games or artificial intelligence is not necessary. Contact: battlecode@mit.edu *** ### 6.148 Web Lab: A Web Programming Class and Competition Shannen Wu Meets Mon-Fri; Jan 4th-15th, 1pm-5pm; Virtual The remaining two weeks comprise grading, judging, and hackathons Office Hours MW TBA Awards Ceremony TBA Pre-register on WebSIS and attend first class. Listeners allowed, space permitting. Prereq: None Level: U 6 units Graded P/D/F Can be repeated for credit Limited to 250 participants. 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. Contact: weblab-staff@mit.edu *** ### 6.176 Pokerbots Competition Shreyas V. Srinivasan, Stephen Otremba Meets M,W,F from Jan 4th-15th 1:00pm- 2:30pm, Virtual Final presentation TBA. Preregister on WebSIS and attend a class in the first week. Listeners allowed, space permitting Prereq: 6.0001 or knowledge of the following: Java, C, C++. No poker experience needed. Level: U 6 units Graded P/D/F Can be repeated for credit 6.176 is a computerized poker tournament. Teams of one to four students compete over the month to build the best possible autonomous poker player for our poker variant announced at the start of IAP. Showcase your programming and strategy skills, applying concepts from math, computer science, and game theory to win prizes from this year's$30,000 plus prize pool! Class provides instruction in bot programming, game theory, probability and statistics, and machine learning, over six lectures which take place during the first two weeks of IAP. Attendance not mandatory but highly encouraged; lecture notes are distributed through the course for people who wish to participate remotely. Concludes with a final competition event and prizes. Sponsored by many of the world's top quantitative trading firms.

Contact:  pokerbots@mit.edu

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### 6.914  Project Engineering

Leo McGonagle

Offered under: 6.914, 16.669)
Meets: Jan 21st -29th. Lab: MTWRF2 Virtual

Final presentation Jan 29th

Prereq: (6.902 and (6.911 or 6.912)) or permission of instructor
Level: U 4 units Graded P/D/F

Students attend and participate in a four-day off-site workshop covering an introduction to basic principles, methods, and tools for project management in a realistic context. In teams, students create a plan for a project of their choice in one of several areas, including: aircraft modification, factory automation, flood prevention engineering, solar farm engineering, small-business digital transformation/modernization, and disaster response, among others. Develops skills applicable to the planning and management of complex engineering projects. Topics include cost-benefit analysis, resource and cost estimation, and project control and delivery which are practiced during an experiential, team-based activity. Case studies highlight projects in both hardware/software and consumer packaged goods. Preference to students in the Bernard M. Gordon-MIT Engineering Leadership Program.

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### 6.S086  Transcribing Prosodic Structure of Spoken Utterances with ToBI

Dr. Stefanie Shattuck-Hufnagel, Dr. Alejna Brugos (Boston University), Dr. Nanette Veilleux (Simmons College).

Meets   TuTh  Jan 5th-28th, noon-2pm, Virtual

Prereq: linguistics, acoustic or psycholinguistics or speech science background suggested

Level: U (6 units) Graded P/D/F

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.S087   Mathematical Methods for Multidimensional Statistics

Farrell Eldrian Wu, Jerry Han, Hoi Wai Yu

Meets  MWF January 4th-January 29th. 2pm-3:30pm, Virtual

Prereq: 6.041A, 18.02.

Level: U (6 units) Graded P/D/F

Covers the mathematical foundations behind many of the methods used in multi-dimensional statistics, machine learning and data science. This includes multidimensional functions and spaces, vector and matrix calculus, constrained and unconstrained optimization and matrix decompositions.  Lectures develop the theoretical foundations of these concepts, while problem sets provide examples of their applications to statistics and machine learning. 6.041B helpful.

Contact: 6s087-staff@mit.edu

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### 6.S088     nanoStories: Workshop on Science Communication at the Nanoscale

Meets Jan 11, 13, 15, 19, 20, 22 from 3pm to 5pm, Virtual

Level: U (3 units) Graded P/D/F

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.

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### 6.S089    Introduction to Quantum Computing

Amir Karamlou

Meets  Jan 4th-Jan 29th, MWF 3pm-5pm,  Virtual

Preregister on WebSIS and email instructor. Listeners permitted.

Prereq:  None

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 (first week), survey quantum circuits (second week), and introduce important quantum algorithms (third week). In the final week we will survey advanced topics such as quantum error correction and quantum communication as well as applications to fields ranging from machine learning to chemistry. This course is self-contained and does not require any prior knowledge of quantum mechanics.

Contact Amir Karamlou, karamlou@mit.edu

***

### 6.S090   Deep Learning for Control

Pulkit Agrawal, Jacob Huh, Anurag Ajay, Zhang-Wei Hong, Andi Peng, Aviv Netanyahu, Xiang Fu, Anthony Simeonov, Tao Chen, Avery Lamp

Meets  Jan 11, 14, 19, 21, 25, 28 (Monday/Thursdays other than Tuesday for the week of MLK Day) 1-3pm EST,  Virtual

Preregister on WebSIS. Listeners permitted.

Prereq:  None

Subject description: An overview of current deep reinforcement learning methods, challenges, and open research topics. The course will be taught by current members of the Improbable AI Lab at CSAIL, with the goal of providing a “bootcamp” for those wishing to get up to speed on current work in Robotics and Deep RL. Weeks 1-2 will detail understanding intelligence via machine learning to deep reinforcement architectures and frameworks (including methods for learning from demonstrations and practical RL). Week 3 will focus on learning for robotics and designing for efficient deep learning infrastructures.

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### 6.S095     Intermediate Probability Problem Solving Techniques

Farrell Eldrian Wu, Jerry Han, Hoi Wai Yu

Meets  Jan 4th-Jan 29th, TuTh 7.30-9 PM, Virtual

Preregister on WebSIS and email instructor. Listeners permitted.

Prereq:  None

This subject is a survey of problem solving techniques in probability, random variables, and stochastic processes. It intends to enhance understanding and intuition of the topics of a standard undergraduate probability class towards solving more involved problems. It covers probability theory, computational techniques, as well as applications to adjacent fields such as statistics and non-deterministic algorithms. The intended audience is students who have taken a class on probability such as 6.041, 6.042, or 18.600, and hopes to build a better problem-solving toolkit for advanced classes involving probability such as 6.046, 6.437, 6.438, 6.262, or 6.265. The first hour of each of the eight two-hour sessions will be a lecture presenting the techniques and examples, while the second hour will be an interactive problem-solving session in small groups.

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### 6.S098    Tethics

Christabel J Sitienei

Meets  Jan 4th-Jan 29th, TuTh 1p-2.30p, Virtual

Preregister on WebSIS

Prereq:  None

Most classes at MIT focus heavily on technical components of technologies without fully placing technology where they exist - within society. The goal of this class is to augment the hard-core technical components taught in most Course 6 classes with social and ethical components to fully place the role of tech in society. It seeks to place technologies right at the heart of society as opposed to in isolation. It presents current affairs in technologies to understand what the code students write will do and nudges students to think more deeply about the ethical implications of technologies they build.

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### 6.S187    Code for Good

Yunxing Liao

MWF 3-5pm; Jan 4th-29th, Virtual

Preregister on WebSIS.

Prerequisite: programming experience necessary

Level: U 6 units

Graded P/D/F. Can be repeated for credit

Limited to 50 participants.

6.S187 provides opportunities for students to work on software-related projects with nonprofit organizations and provides them with technical expertise. Teams of 3-5 students will be matched with a nonprofit that has a project that is of interest to the student. Students will be mentored by a representative from the organization and subject instructors. Students can sign up as individuals or in groups. Project listings and detailed information available on the website:

http://codeforgood.mit.edu/programs/iap-class/

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

Contact: codeforgood@mit.edu

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### 6.S191   Introduction to Deep Learning

Alexander Amini, Ava Soleimany

Mon Jan 18 - Fri Jan 29th, 1pm-3pm, Virtual

Preregister on WebSIS and attend first class. Listeners allowed

Prereq: 18.06 and familiarity with Python helpful but not required

Level U, 6 units, Graded P/D/F

Limited to 350

Intro to deep learning methods with applications to machine translation, image recognition, game playing, image generation and more. A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures. Course concludes with project proposals with feedback from staff and panel of industry sponsors.

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### 6.S192    Deep Learning for Art, Aesthetics, and Creativity

Ali Jahanian

Fri Jan 15th - Fri Jan 29th, 12-2, Virtual

Preregister on WebSIS

Level U, 3 units, Graded P/D/F

This course offers both concepts and technical aspects of design of AI in the domains of arts and visual design. We will cover state-of-the-art research in using neural networks, generative models, visualization, and network interpretability for art, design, and aesthetics. Applications will cover topics that can potentially flourish students’ artistic creativity, including, prediction of aesthetics rating of images, neuro-style transfer, fashion, and analysis/synthesis of collateral design, infographics, web design, and photos. We will also have speakers who can share their research on related topics. This course is designed for those who have some knowledge about deep learning and would like to obtain perspective in how to think creatively and apply AI to new problems in art and creativity.