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Payment Status. Wire Transfer Instructions.Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real problems. This course will introduce supervised learning decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learningunsupervised learning clustering, dimensionality reductionand reinforcement learning.
Additionally, the course will discuss evaluation methodology and recent applications of machine learning, including large scale learning for big data and network analysis. Prerequisites: CIS or equivalent programming experience. This section briefly describes the differences between these courses. The course is cross-listed between undergraduate and graduate versions; the graduate course has somewhat different requirements as described below.
Just because it is listed as "introductory" does not necessarily mean that it is "easier". CIS Machine Learning is a more mathematically rigorous course in statistical machine learning that provides the background necessary to design and use new ML algorithms.
Consequently, CIS requires students to have basic knowledge of linear algebra matrices, eigenvectors, etc. It uses Matlab and is said to require a lot of work, but prepares students to conduct ML research. Students registered for the graduate version of this course CIS will be required to complete additional work throughout the semester.
This work will include additional components to the homework, additional requirements on the course project, and possibly different or additional exam questions. Since the two versions have different requirements, you cannot complete the course as CIS and later petition to have it changed to CIS for graduate credit; if you're considering changing this course to CIS for graduate credit, you should register for the graduate version now.
This is a collection of readings that will be used throughout the course. There is NOT a single reading packet you need to obtain -- readings will be distributed incrementally throughout the semester, either in hard-copy or posted online throughout the course. For a more advanced treatment of machine learning topics, I would recommend one of the following books:. Attendance and active participation are expected in every class.
Participation includes asking questions, contributing answers, proposing ideas, and providing constructive comments. As you will discover, I am a proponent of two-way communication and I welcome feedback during the semester about the course. I am available to answer student questions, listen to concerns, and talk about any course-related topic or otherwise! Come to office hours! This helps me get to know you. You are welcome to stop by and chat. There are many more exciting topics to talk about that we won't have time to cover in-class.
Please send all course communications through Piazza. Your post should be public for general questions, private to all instructors which includes the TAs for any student-specific issues e.
Eaton for extremely personal matters. Although computer science work can be intense and solitary, please stay in touch with me and the TAs, particularly if you feel stuck on a topic or project and can't figure out how to proceed. Often a quick face-to-face conference or Piazza post can reveal solutions to problems and generate renewed creative and scholarly energy.
It is essential that you begin assignments and projects early, since we will be covering a variety of challenging topics in this course. Your grade will be based upon eight homework assignments, quizzes, a final exam, and a course project. Assignments must be submitted according to the assignment submission instructions.
At the end of the semester, final grades will be calculated as a weighted average of all grades according to the following weights:. Incomplete grades will be given only for verifiable medical illness or other such dire circumstances. Here is how the percentage grades will map to final letter grades; percentages are not rounded:. There will be one final exam in this course.This course studies the cinematic representation of civil wars, ethnic conflicts, nationalistic doctrines, and genocidal policies.
The focus is For important registration dates and deadlines, see the Academic Calendar. This table provides you with information on the LPS policies for course registration and quotas for our courses. We will update this web page daily if any changes in course status occur. Students who have been closed out of classes by quotas or who are attempting to register for classes that are full are advised to register for backup courses, to continue to try to register for their preferred courses and to check this page regularly for any course updates.
If sections are closed, students will not be able to register. If the course appears open, but you are told you cannot register, no permits will be granted as the remaining seats have been reserved for LPS students.
If you have further questions please contact: lps sas. Download Excel File. Skip to main content. Penn is committed to maintaining a safe campus and workplace for faculty, students, staff, and visitors. While we are not onsite, we are still available Monday through Friday from 9 a. Visit coronavirus. Search form Search. Spring Courses.
Featured Spring Courses. Nationalism and Ethnic Conflict in Film.Altuzarra - Spring Summer 2020 - Full Show
African Art. This selective survey examines a variety of the circumstances of sub-Saharan African art, ranging from imperial to nomadic cultures and from Time of Day - Any - ampm ampm ampm pmpm pmpm pmpm pmpm pmpm pmpm pmpm pmpm pmpm pmpm pmpm. Online - Any - No Yes. Permit Queries for Undergraduate Courses. Report Accessibility Issues and Get Help.This course will cover selected topics on privacy-enhancing technologies.
The first two-thirds of the course will primarily discuss systems that preserve privacy with the use of cryptography. These systems include encrypted databases, anonymous networks, blockchains, machine learning on encrypted data, among others.
We will also discuss attacks on these systems. These systems use a variety of amazing building blocks including public key encryption, order-preserving encryption, homomorphic encryption, functional encryption, private information retrieval, oblivious RAM, secret sharing, oblivious pseudorandom functions, garbled circuits, and differential privacy.
The last part of the course will cover zero-knoweldge succinct non-interactive arguments of knowledge zkSNARKs. We will cover interactive proofs, sum check protocols, arithmetic cirtcuits, polynomial commitments, and finally the Hyrax zkSNARK protocol. This course is discussion-based and every student is expected to read the paper s assigned for each lecture. Provide a written critique of three of the assigned readings.
Each critique consits of a summary of the assigned paper, a discussion of its limitations, a list of applications, and extensions. Research project. Propose and complete a research project in a related area can be done in pairs.
Example projects include but are not limited to :. Students should submit a project proposal by February 21and are encouraged to discuss with me their ideas prior to selecting a project. Students are expected to give an oral presentation of their project in class and turn in a final report by May 1. Students with a strong math background who are willing to learn some of the basics on their own may also take this course.
Untraceable Electronic Cash. How to share a secret.
How to broadcast a secret. Elliptic Curve Cryptography: a gentle introduction. Elliptic Curve Cryptography: finite fields and discrete logarithms. Delegating Computation: Interactive Proofs for Muggles.This degree is the first specialized program in the country covering every aspect of Cyber Physical System CPS development, from theoretical fundamentals to state-of-the-art hardware and software technologies.
Other institutions may offer coursework or a track in embedded systems, which offers exposure at a high-level, but not to the degree of specialization that EMBS provides. We started this program because we recognized that an electrical engineering or computer science degree was not enough to work with embedded systems.
Students really need in-depth exposure to all aspects of integration, not just examples. In our program, students will learn how to design, model, and implement embedded systems using industry-standard formal methodologies, and then verify, evaluate, and deploy these systems in real-world scenarios.
Students could also partake in many large-scale CPS projects with our world-renowned faculty. EMBS enrollment is highly competitive. Our very selective enrollment promotes a cohesive and collaborative learning environment, allowing greater faculty involvement in student advising. EMBS graduates are in high demand. Any student wishing to complete a master's thesis may enroll in two course units of CIS The student first chooses a thesis advisor, who must belong to the CIS Graduate Group, proposing a suitable thesis topic.
The thesis advisor and student discuss and determine the final topic, scope, etc.
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The advisor and student also determine one other faculty member to be a reader for the thesis. Once the advisor, reader, and topic has been chosen, the student sends an email containing a one page proposal to both the EMBS Program Director and Associate Director. The advisor and reader will evaluate the thesis and make the determination of its suitability as a research document.
An oral presentation of the thesis is required. This can take the form of a public presentation open to all CIS faculty and students to attend, or in lieu of that, a conference presentation or poster presentation decided by the thesis adviser.
The final version of the thesis with all signatures, and a photocopy of the thesis both unboundare submitted to the SEAS Graduate Office, Towne. Masters Program. Program Director. Advising Hours. Associate Director. Where did our graduates go? Core Courses. Recommended Electives. Course Descriptions. Reference Links. Who should apply? Graduate Academic Calendar. CIS Advising. Latest Project: Demand Response video. Autonomous Racing Club. Medical Device Club.
Billing Schedule. JP Eckert Fellowship for U. Citizens or U. Permanent Residents. Forms for Graduate Students.Students can post questions and collaborate to edit responses to these questions. Instructors can also answer questions, endorse student answers, and edit or delete any posted content.
Piazza is designed to simulate real class discussion. It aims to get high quality answers to difficult questions, fast! The name Piazza comes from the Italian word for plaza--a common city square where people can come together to share knowledge and ideas.
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These allowances are to accommodate things like mild sickness, interviews, computer crashes see belowprocrastination, etc. No additional late days will be granted for any of the above reasons. If you are out of late days, then your homework will receive a score of 0. Late homeworks can be turned in via Gradescope.
Exceptions will be considered in special circumstances. Please contact the professor and Head TAs.
We highly recommend using Overleaf for typing up written homeworks. For programming homeworks, we recommend backing up your computer regularly Google, Microsoft OneDrive, etc.
You can also use GitHub, though make sure you only use private repos. You are allowed to discuss ideas for solving homework problems in groups of three, documenting who you discussed with at the top of your assignment. These students must also list you as a collaborator. The composition of your team may change from one homework to another. You are not allowed to write up the solutions together. You are allowed to discuss low-level issues like the meaning of Java constructs, or how to use the computing environment.
At no time — prior to submitting an assignment, after submitting an assignment, or after the course has ended — are you allowed to….
Each written assignment and each midterm exam will have a regrade request deadline that will be posted typically one week after the assignment or exam has been returned with your score. Beyond that deadline, regrades can only be given by permission of the instructor. Regrade requests are meant for cases such as arithmetic mistakes in tabulating a final score, or for a grader not seeing that you continued your solution on the back of the page as an example.
In the interest of transparency, the course staff makes its best effort to indicate exactly where points are docked with the associated reasons. For the written assignments and for the midterms, solutions will be provided in class.
Please consult these carefully before requesting a regrade. Please submit a regrade request via Gradescope by the regrade deadline. Regrade requests will only be accepted when they are a result of our mistake.