Accelerate your career with a computer science program. Tuesday, Thursday The course examines the mathematical and algorithmic foundations of democracy, running the gamut from theory to applications. Many science communities are combining high performance computing and high-end data analysis platforms and methods in workflows that orchestrate large-scale simulations or incorporate them into the stages of large-scale analysis pipelines for data generated by simulations, experiments, or observations. Topics include C and assembly language programming, program optimization, memory hierarchy and caching, virtual memory and dynamic memory management, concurrency, threads, and synchronization. Supervision of experimental or theoretical research on acceptable problems in computer science and supervision of reading on topics not covered by regular courses of instruction. Tuesday, Thursday In addition, labs will have access to state-­‐of-­‐the-­‐art IoT devices and 3D cameras for data acquisition. Emphasis will be given to the strengths, trade-offs, and limitations of each method to highlight the importance of merging analytical skills with critical quantitative thinking. We are excited to offer a series of introductory CS50 courses and Professional Certificate programs from Harvard that are open to learners of all backgrounds looking to explore computer science, mobile app … Tuesday, Thursday Browse the latest free online courses from Harvard University, including "CS50's Introduction to Game Development" and "CS50's Web Programming with Python and JavaScript." Tuition for one course* $6,366. Provides the intellectual tools needed to design, evaluate, choose, and use programming languages. 1:30pm to 2:45pm. Class topics are reinforced through a series of intensive programming assignments which use a real operating system. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web programming. The curriculum for this course builds throughout the academic year. Design and analysis of efficient algorithms and data structures. Third, blockchain technology, which underlies Bitcoin, creates a new trusted network infrastructure for many new distributed applications. Monday, Wednesday This is an applications course highlighting the use of modern computing platforms in solving computational and data science problems, enabling simulation, modeling and real-time analysis of complex natural and social phenomena at unprecedented scales. Part one of a two part series. Emphasis will be on evolution and neuroscience, but other topics such as development will be also discussed. Topics on the design and analysis of algorithms, processes, and systems related to crowds and social networks. The course is lab- and project-based, primarily in small teams, and culminates in the building and testing of a question-answering system. Cynthia Dwork, Martha Minow Website built using Hugo generator based on the learn theme Computational Neuroscience. Second, cybersecurity and privacy will receive unprecedented attention from the industry. 10:30am to 11:45am. 1:30pm to 2:45pm. Advance your career as a software developer and learn programming with free courses from the world’s top universities. Are there encryption schemes that can't be broken? It focuses on digital devices and systems, and it complements ENG-SCI 152, which focuses on devices and systems that use analog electronics. David Parkes, Finale Doshi-Velez Salil Vadhan These techniques will lay the foundation for future computational libraries and packages for both high-­‐performance computing and energy-­‐efficient devices. The course includes several small projects which give students hands-on experience with various offensive and defensive techniques; the final, larger project is open-ended and driven by student interests. Tuesday, Thursday Graphics, Vision, and Visualization. 10:30am to 11:45am. 12:00pm to 1:15pm. The class will include an introduction to the community through virtual talks and interactive Q&As with regular course guests. Students work as a team with a client on a real-world open-ended problem, and gain experience in Computer Science (problem definition, software development, iterative design), and in other fields relevant to the problem. Tiny machine learning (TinyML) is defined as a fast-growing field of machine learning technologies and applications including hardware (dedicated integrated circuits), algorithms and software capable of performing on-device sensor (vision, audio, IMU, biomedical, etc.) Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. This course focuses on the design and implementation of modern operating systems. Computer science therefore offers a top-down approach to understanding what could possibly be computed in biology, and how. See page 8 for popular study cards. Topics include computational social choice (identifying optimal voting rules), fair division with applications to political redistricting (avoiding gerrymandering) and apportionment (allocating seats on a representative body), sortition (randomly selecting citizens' assemblies), liquid democracy (transitively delegating votes), and weighted voting games (analyzing legislative power through cooperative game theory). For more course information can be found at http://pl-ai-seminar.seas.harvard.edu/, Stephen Chong This course covers the fundamentals of 3D computer graphics using a modern shader-based version of OpenGL. Readings in AI, theoretical CS, machine learning, social science theory, economic theory, and operations research. Industry partners will support the course by giving guest lectures and providing resources. This course introduces fundamentals in designing and building modern information devices and systems that interface with the real world. Comprehensive introduction to the principal features and overall design of both traditional and modern programming languages, including syntax, formal semantics, abstraction mechanisms, modularity, type systems, naming, polymorphism, closures, continuations, and concurrency. Mathematics with Computer Science (Course 18- C) Physics (Course 8) Interdisciplinary Programs; Chemistry and Biology (Course 5- 7) Computation and Cognition (Course 6- 9) Computer Science and Molecular Biology (Course 6- 7) Computer Science, Economics, and Data Science (Course 6- 14) An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with … The objective can be for high-­‐performance computing and energy-­‐efficient computing (“green” data center servers as well as small embedded devices). Explores problem-solving and data analysis using the Python programming language; presents an integrated view of computer systems, from switching circuits up through compilers and object-oriented design. This course teaches students how to think algorithmically and solve problems efficiently. Milind Tambe, Boaz Barak Monday, Wednesday Fundamental concepts in the design of computer programs, emphasizing the crucial role of abstraction. After having successfully taken this course, students will acquire an integrated understanding of these issues. We will discuss the recent technology trends and design choices of performance, scalability, manageability, and cost faced by companies who own large-scale networks such as Amazon, Google, Microsoft, and Facebook. Economics and Computation. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set;  (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. In many of these processes the actual steps taken by biological systems are not currently understood. The ability to connect devices over long distances, via the internet, changed our world. 3:00pm to 5:45pm. Special Concentrations. The curriculum for this course builds throughout the academic year. Discover both on-campus and online courses. 10:30am to 11:45am. Stephanie Gil What is privacy, and how is it affected by recent developments in technology? 3:00pm to 4:15pm. Environmental Science and Public Policy. Theory of Communication. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. The goal of the course is to give students insight into the difference between programming and programming well. Students read and present research papers, undertake a research project. Covers topics related to algorithms for big data, especially related to networks and database systems. Tuesday, Thursday Computer networking has enabled the emergence of mobile and cloud computing, creating two of the most significant technological breakthroughs in computing. Course relies on some technical material, but is open and accessible to all students, especially those with interest in economics, engineering, political science, computer science, sociology, biology, law, government, philosophy. We gain clarity of semantics, algorithms and purpose. Monday, Wednesday To emphasize the differing approaches to expressing programming solutions, you will learn to program in a variety of paradigms -- including functional, imperative, and object-oriented. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Talks at Morning Prayers. Computer networks have become even more critical these days since remote activities have become a new norm. Monday, Wednesday In this course, we will discuss the successful deployments and the potential use of AI in various topics that are essential for social good, including but not limited to health, environmental sustainability, public safety and public welfare. 3:00pm to 4:15pm. Both student participation in the classroom and effective teamwork outside the classroom are stressed. Seminar course exploring recent research in programming languages. Tuesday, Thursday Tuesday, Thursday ; Software abstractions and programming models: MapReduce (PageRank, etc. Introduction to the intellectual enterprises of computer science and the art of programming. Nicole Immorlica, Brendan Lucier Principal techniques will come from cryptography, differential privacy, and the newly emerging areas of adaptive data analysis and algorithmic fairness. Plus take any of Math 1a, Math 1b, and CS20 as needed. Two thirds of CS50 students have never taken CS before. Monday, Wednesday Monday, Wednesday In general current understanding of most aspects of biology is not complete or specific enough to provide theories in which predictions can be made by analysis or computer simulation. This course picks up where CS50 leaves off, diving more deeply into the design and implementation of web apps with Python,... An introduction to the intellectual enterprises of computer science and the art of programming. Many processes in biology consist of step by step processes, whether in evolution, neural activity, development, or protein circuits. We will ensure students have access to the appropriate computational resources (i.e., GPUs). The course is a journey into the foundations of Parallel Computing at the intersection of large-scale computational science and big data analytics. Multi-robot systems are becoming more pervasive; from future autonomous vehicle fleets, to drones, to manufacturing robots. 25,464. James Waldo Review of the fundamental structures in modern processor design. Topics covered in this course are broadly divided into 1) planning and search algorithms, 2) probabilistic reasoning and representations, and 3) machine learning (although, as we will see, it is impossible to separate these ideas so neatly). Vijay Janapa Reddi Monday, Wednesday, Friday Information and Society. This year only: Students will read and discuss HCI papers about computers working with---or clashing against---the strengths and weakness of human cognition, e.g., the positive and negative impacts of AI recommendation systems and the impact of interruptions on continuity of thought. Tuesday, Thursday Problem sets inspired by the arts, humanities, social sciences, and sciences. Tuesday, Thursday Chemical and Physical Biology. 9:00am to 10:15am. Monday, Wednesday, Friday Students also learn how virtualization allows a physical machine to partition its resources across multiple virtual machines. Johanna Beyer ... Real college courses from Harvard, MIT, and more of the world’s leading universities. In addition to the theoretical lectures, the course will involve a programming component aiming to get students to the point where they can both reproduce results from papers and work on their own research. Tuesday, Thursday Tuesday, Thursday 10:30am to 11:45am. 4:30pm to 5:45pm. 12:00pm to 1:15pm. Tuesday, Thursday Tuesday, Thursday An introduction to key design principles and techniques for visualizing data. Scaling computation over parallel and distributed computing systems is a rapidly advancing area of research receiving high levels of interest from both academia and industry. 10:30am to 11:45am. This will be a graduate level course on recent advances and open questions in the theory of machine learning and specifically deep learning. Tuesday, Thursday The course will also include an off-line component primarily consisting of select broad-interest CS research readings and writing assignments. 12:00pm to 1:15pm. 12:00pm to 1:15pm. We will review both classical results as well as recent papers in areas including classifiers and generalization gaps, representation learning, generative models, adversarial robustness and out of distribution performance, and more. Artificial Intelligence. The specific challenge for Fall 2020 will be announced on the course website. Practice in reasoning formally and proving theorems. A student wishing to enroll in Computer Science 91r must be accepted by a faculty member who will supervise the course work. Take at least two of CS50, CS51, and CS61; take CS121 and another “theory” course; take four technical electives; and take Math 21a and Math 21b. Computational and Data Science. Natural-language-processing applications are ubiquitous: Alexa can set a reminder if you ask; Google Translate can make emails readable across languages; Watson outplays world Jeopardy champions; Grover can generate fake news, and recognize it as well. You will work with ideas from linguistics, statistical modeling, and machine learning, with emphasis on their application, limitations, and implications. Tuesday, Thursday Additional Resources for CS concentrators and pre-concentrators, including course listings and links to useful guides, forums, and mailing lists. In this advanced topic course, we will look at artificial intelligence broadly construed from the point of view of programming languages. Some of the questions we will touch upon include: Are there functions that cannot be computed? This course critically examines popular concepts of privacy and uses a rigorous analysis of technologies to understand the policy and ethical issues at play. 1:30pm to 2:45pm. Boaz Barak 9:00am to 10:15am. Covers design practices, data and image models, visual perception, interaction principles, visualization tools, and applications. Social Studies. Course culminates in a final project. We expect several focuses in the coming years. 6.01 Introduction to EECS via Robotics. Monday, Wednesday As a result, the question of how to control, coordinate, and secure these systems has been a growing topic in the robotics literature in recent years. In this course, students will learn principled methods of mapping prototypical computations used in machine learning, the Internet of Things, and scientific computing onto parallel and distributed compute nodes of various forms. CS50's Introduction to Game Development. Tuesday, Thursday Computation occurs over a variety of substrates including silicon, neurons, DNA, the stock market, bee colonies and many others. Students will use open source tools and libraries and apply them to data analysis, modeling, and visualization problems. Topics vary from year to year. 1:30pm to 2:45pm. This online program helps participants understand building technology and its application within their real estate projects. Main topics include: geometric coordinate systems and transformations, keyframe animation and interpolation, camera simulation, triangle rasterization, material simulation, texture mapping, image sampling and color theory. Software Development — 57 Courses. The form must be filled out and signed by the student and faculty supervisor. 1:30pm to 2:45pm. You will learn how to uncover needs that your customers cannot even articulate. Eigenvectors and eigenvalues of graphs and their applications to computer science problems, such as clustering, solving linear systems, derandomization, sampling via MCMC, counting, web search, and maximum flow. As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers (end users) correctly understand and consequent trust the functionality of these models. Computer science is not so much the science of computers as it is the science of solving problems using computers. Requires a major final research-based project. Students will be required to produce non-trivial programs in Python. Hima Lakkaraju 10:30am to 11:45am. Recent years have seen AI successfully applied to societal challenge problems; indeed, it has a great potential to provide tremendous social good in the future. Monday, Wednesday 10:30am to 11:45am. 3:00pm to 4:15pm. Research papers that address some aspect of the complexity question, whether by mathematical analysis, computer simulations or experimental findings will be discussed. Ruby on Rails: An Introduction from Johns Hopkins University … A key part of this course will be to start AI4SI projects with local area non-profits. Consideration is given in design to interactions between hardware and software systems. This graduate level course aims to familiarize students with the recent advances in the emerging field of explainable ML. The course discusses threads, processes, virtual memory, schedulers, and the other fundamental primitives that an OS uses to represent active computations. Tuesday, Thursday Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design ... CS50's Web Programming with Python and JavaScript. Emphasis on a quantitative evaluation of design alternatives and an understanding of performance and energy consumption issues. Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. Additional information and a form are available via https://harvardcs.info/forms/#cs-91r-form. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning. This is a self-paced course–you may take CS50x on your own schedule. 10:30am to 11:45am. Programming Languages drive the way we communicate with computers, including how we make them intelligent and reasonable. In lieu of typical on-campus interactions that normally occur during the first year of the PhD program, this course provides an opportunity for entering CS PhD students to engage with the Harvard CS community and to build a cohort among the entering PhD students. Institute LAB. The goal is to provide students with a rigorous perspective on, and a technical toolbox for, the design of better democratic systems. Tuesday, Thursday The course covers skills and techniques necessary to design innovative interactive products that are useful, usable and that address important needs of people other than yourself. The goal of this course is to introduce the ideas and techniques underlying the design of computer systems that make intelligent decisions based on data. 1:30pm to 2:45pm. PhDs I have supervised. We will focus on challenges in “AI for Social Impact” (AI4SI), what makes projects successful, and why projects fail. This course will take a holistic approach to helping students understand the key factors involved, from data collection and exploratory data analysis to modeling, evaluation, and communication of results. With a team of extremely dedicated and quality lecturers, harvard cs course list pdf will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves. Read the latest updates on coronavirus from Harvard University. Hanspeter Pfister, Liberty Vittert Paper-based seminar course that introduces students to the state of the art in systems research through historical and quantitative lenses. Case studies: database anonymity, research ethics, wiretapping, surveillance, and others. Cost: Free; $199 for a certificate. Monday, Wednesday, Friday Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, and software engineering. This course teaches students how to think algorithmically and solve problems efficiently. Move your organization toward an AI-based model, unleash the potential of AI, and create opportunities for building competitive... Everything you want to know about AI in health care, but are afraid to ask. Introduction to the intellectual enterprises of computer science and the art of programming. 12:00pm to 1:15pm. Monday, Wednesday, Friday Students will gain hands-on experience through computing labs. 10:30am to 11:45am. Vijay Janapa Reddi This course teaches students how to think algorithmically and solve problems efficiently. Students will have latitude in choosing a final project they are passionate about. Languages include C, Python, and SQL plus HTML, CSS, and JavaScript. Displaying results 1 to 50 of (132) 1 2 3 01:00pm - 03:59pm Autonomous Robot Systems COMPSCI 189 Nagpal FAS Computer Science 2016 Spring Designed for concentrators and non-concentrators alike, with or without prior programming experience. To master the subject, students will need to appreciate the close interactions between computational algorithms, software abstractions, and computer organizations. In this … History and Literature. Folklore and Mythology. Paid transcript and academic credit via Harvard Extension School; The courses is also part of edX’s Professional Certificate in Computer Science for Web Programming. David Malan, Brian Yu Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web programming. We will cover a wide gamut of applications from control of groups of flying drones, to decision making in autonomous car networks, to space exploring CubeSats. Milind Tambe 3:00pm to 4:15pm. The course will use server clusters at Harvard as well as external resources in the cloud. Tuesday, Thursday Take course on. Pavlos Protopapas, Kevin A. Rader, Christopher Tanner In this course, we will review seminal position papers of the field, understand the notion of model interpretability from the perspective of decision makers (end users), discuss in detail different classes of interpretable models (e.g., case (prototype) based approaches, sparse linear models, rule-based techniques, saliency maps, generalized additive models, and counterfactual explanations), and explore the connections between model interpretability and causality, debugging, and fairness. Elena Glassman Tuesday, Thursday Topics include computer organization, memory system design, pipelining, and other techniques to exploit parallelism. See Computer Science under Fields of … Monday Reading and discussion will be based on a selection of papers, suggested collectively. Tuesday, Thursday Are there true mathematical statements that can't be proven? 1:30pm to 2:45pm. This course explores practical attacks on modern computer systems, explaining how those attacks can be mitigated using careful system design and the judicious application of cryptography. Students will work on a semester-long visualization project that will allow them to visualize their own data sets and write a short paper about their project. You will also have several opportunities to formally communicate your design ideas to a variety of audiences. More information can be found at https://github.com/minlanyu/cs145-site. Big data science adds the ‘fourth pillar’ to scientific advancements, providing the methods and algorithms to extract knowledge or insights from data. Wednesday Henry Leitner For many computational outcome specifications it is known or believed that no mechanism with feasible resources can realize them. Study of Religion. Students typically read and present research papers, undertake a research project.For Spring 2021, we will examine a variety of advanced topics, including dependent types, logical relations, and module systems. Monday, Wednesday CS50's Web Programming with Python and JavaScript, CS50's Introduction to Artificial Intelligence with Python, From Smart to Autonomous: Emerging Technologies in Buildings, Applied Artificial Intelligence for Health Care, Designing and Implementing AI Solutions for Health Care. 9:00am to 10:15am, Pavlos Protopapas, Mark Glickman, Christopher Tanner Data science combines data, statistical analysis, and computation to gain insights and make useful inferences and predictions. CS50 Introduction to Game Development — CS50G. Introduction to Computer Science from Harvard, better known as CS50, is the largest course on the Harvard campus and more than 2,000,000 learners worldwide have registered for the course on edX. David Sondak They will formulate their projects early in the course, so there will be sufficient time for discussion and iterations with the teaching staff, as well as for system design and implementation. An exploration of the system call interface explains how applications interact with hardware and other programs which are concurrently executing. Start your search today. CS50 … This Harvard seminar will be coordinated with a "sister seminar" at MIT, taught by Ankur Moitra. 1:30pm to 2:45pm. Examines theoretical and practical limitations related to unsolvable and intractable computational problems, and the social and ethical dilemmas presented by such issues as software unreliability, algorithmic bias, and invasions of privacy. Students with mathematical inclinations and exposure to graph theory, probability theory, linear algebra, and algorithms will derive the most benefit from this course. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Committees and Boards. Elena Glassman Minlan Yu The course will also emphasize on various applications which can immensely benefit from model interpretability including medical imaging and judicial decision making. Learn to use machine learning in Python in this introductory course on artificial intelligence. Harry Lewis The class will be organized into the following modules: Big picture: use of parallel and distributed computing to achieve high performance and energy efficiency; End-­‐to-­‐end example 1: mapping nearest neighbor computation onto parallel computing units in the forms of CPU, GPU, ASIC and FPGA; Communication and I/O: latency hiding with prediction, computational intensity, lower bounds; Computer architectures and implications to computing: multi-­‐cores, CPU, GPU, clusters, accelerators, and virtualization; End-­‐to-­‐end example 2: mapping convolutional neural networks onto parallel computing units in the forms of CPU, GPU, ASIC, FPGA and clusters; Great inner loops and parallelization for feature extraction, data clustering and dimension reduction: PCA, random projection, clustering (K-­‐means, GMM-­‐EM), sparse coding (K-­‐SVD), compressive sensing, FFT, etc.
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