The rapid growth in natural language processing (NLP) over the last couple years has generated student interest and excitement in learning more about the field. In this paper, we present two types of students that NLP courses might want to train. First, an "NLP engineer" who is able to flexibly design, build and apply new technologies in NLP for a wide range of tasks. Second, an "NLP scholar" who is able to pose, refine and answer questions in NLP and how it relates to the society, while also learning to effectively communicate these answers to a broader audience. While these two types of skills are not mutually exclusive -- NLP engineers should be able to think critically, and NLP scholars should be able to build systems -- we think that courses can differ in the balance of these skills. As educators at Small Liberal Arts Colleges, the strengths of our students and our institution favors an approach that is better suited to train NLP scholars. In this paper we articulate what kinds of skills an NLP scholar should have, and then adopt a backwards design to propose course components that can aid the acquisition of these skills.

There were three options for the course final project. Students either chose their own topic as a custom final project, or else they took part in one of the options for the default final projects, involving building question-answering systems. This year, we had two default final project options: Either people could build from scratch (regular, IID) question-answering models for the SQuAD 2.0 challenge or in the Robust QA track, students started with 3 question-answering datasets (SQuAD, Natural Questions, and NewsQA) and a pre-trained, transformer QA system and worked to produce a system that worked robustly on (OOD) test sets from additional domains. You can find links to previous years' reports under Previous Offerings on the homepage.

As robots evolve, society's collective imagination forever ponders what else robots can do, with recent fascinations coming to life as self-driving cars or robots that can walk and interact with objects as humans do. These sophisticated systems are powered by advances in deep learning that triggered breakthroughs in robotic perception, so that robots today have greater potential for better decision-making and improved functioning in real-world environments. But tomorrow's roboticists need to understand how to combine deep learning with dynamics, controls, and long-term planning. To keep this momentum in robotic manipulation going forward, engineers today must learn to hover above the whole field, connecting an increasingly diverse set of ideas with an interdisciplinary focus needed to design increasingly complex robotic systems. Last fall, MIT's Department of Electrical Engineering and Computer Science launched a new course, 6.800 (Robotic Manipulation) to help engineering students broadly survey the latest advancements in robotics while troubleshooting real industry problems.

Machine-learning algorithms are often referred to as a "black box." Once data are put into an algorithm, it's not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the "black box" problem, through a combination of data science and physics-based engineering. In class 2.C01 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

Machine learning algorithms are often referred to as "black boxes." Once the data is put into the algorithm, it is not always possible to know exactly how the algorithm will reach the prediction. This can be especially frustrating when problems occur. MIT's new Mechanical Engineering (MechE) course teaches students how to combine data science and physics-based engineering to tackle the "black box" problem. In Class 2.C161 (Modeling and Designing Physical Systems Using Machine Learning), Professor George Barbastathis teaches how mechanical engineers use their unique knowledge of physical systems to check algorithms and create more accurate predictions.

In MIT 2.C161, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions. Machine-learning algorithms are often referred to as a "black box." Once data are put into an algorithm, it's not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the "black box" problem, through a combination of data science and physics-based engineering.

The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.

Natural language processing (NLP), or NLP for short, is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. It is broadly defined as the automatic manipulation of natural language, like speech and text, by software or technology. Natural language processing is a form of AI that is easy to understand and start using. It can also do a lot to help you in making better business decisions. In order to make your website worth your user's time, NLP can do help you a lot.

The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.

Deep learning is advancing at lightning speed, and Alexander Amini '17 and Ava Soleimany '16 want to make sure they have your attention as they dive deep on the math behind the algorithms and the ways that deep learning is transforming daily life. Last year, their blockbuster course, 6.S191 (Introduction to Deep Learning) opened with a fake video welcome from former President Barack Obama. This year, the pair delivered their lectures "live" from Stata Center -- after taping them weeks in advance from their kitchen, outfitted for the occasion with studio lights, a podium, and a green screen for projecting the blackboard in Kirsch Auditorium on their Zoom backgrounds. "It's hard for students to stay engaged when they're looking at a static image of an instructor," says Amini. "We wanted to recreate the dynamic of a real classroom." Amini is a graduate student in MIT's Department of Electrical Engineering and Computer Science (EECS), and Soleimany a graduate student at MIT and Harvard University.