2024-2025 Graduate Catalog | | Program DescriptionThe MS in Computer Science offers both a thesis and a non-thesis option. To receive full admission to graduate study in computer science, a student must have taken intermediate-level undergraduate computer science, including computer organization, data structures, operating systems, either algorithms or theory of computation, and either compilers or survey of programming languages; and basic mathematics including discrete mathematics. An applicant with minimal deficiencies may be admitted with prerequisites, while one with several deficiencies may be required to satisfactorily complete prerequisite work as a non-degree student prior to admission as a graduate student. For more information, please visit https://www.clemson.edu/cecas/departments/computing/academics/graduates/programsofstudy/msincs.html . Combined BS/MS in Computer ScienceClemson Computer Science students may begin a Master of Science degree program while completing the Bachelor of Science degree and use a limited number of courses to satisfy the requirements of both their undergraduate and graduate degrees. Students interested in this combined degree program should discuss it with the Computer Science graduate program coordinator and undergraduate program advisor. Students pursuing Senior Departmental Honors should also meet with the Computer Science Honors Coordinator. Application to this program should be made by the end of the junior year but may be made at any time from the junior year until one semester prior to the expected BS graduation. Application details are available in the Undergraduate Catalog. Summary of Degree RequirementsThesis option. The thesis option includes a minimum of 24 hours of graduate credit and a minimum of six hours of master’s thesis research ( CPSC 8910 ). Non-Thesis OptionThe non-thesis option includes a minimum of 30 hours of graduate credit, none of which may be master’s thesis research. There are no specific required courses. Students take three courses in one of the core areas defined in the graduate program handbook as an area of concentration and courses in at least two other core areas for breadth. For the thesis option, at least 21 of the 30 credits must be at the 8000 level or above, which includes the CPSC 8910 credits. For the non-thesis option, at least 18 of the 30 credits must be at the 8000 level or above. Students in the thesis option may include up to nine credits of approved courses from outside the School of Computing. Students in the non-thesis option may include up to 12 credits of approved courses from outside the School of Computing. Outcomes, Learning Objectives, and Graduation RequirementsThe MS in Computer Science has three Student Learning Outcomes: - Students can frame a real-world problem such that it can be addressed computationally.
- Students can evaluate multiple computational approaches to a problem and choose the most effective one. The different approaches may involve different algorithms and/or different software tools.
- Students can apply reasoning and technical skills to solve a computational problem with minimal guidance.
Hallucinating Law: Legal Mistakes with Large Language Models are PervasiveA new study finds disturbing and pervasive errors among three popular models on a wide range of legal tasks. ![non thesis masters computer science in canada Lady justice on digital background](https://hai.stanford.edu/sites/default/files/styles/media/public/2024-01/law%20artificial%20intelligence.jpg?itok=_tlZGLLn) Pitiphothivichit/iStock In May of last year, a Manhattan lawyer became famous for all the wrong reasons. He submitted a legal brief generated largely by ChatGPT. And the judge did not take kindly to the submission. Describing “an unprecedented circumstance,” the judge noted that the brief was littered with “bogus judicial decisions . . . bogus quotes and bogus internal citations.” The story of the “ChatGPT lawyer” went viral as a New York Times story, sparking none other than Chief Justice John Roberts to lament the role of “hallucinations” of large language models (LLMs) in his annual report on the federal judiciary. Yet how prevalent are such legal hallucinations, really? The Legal Transformation The legal industry is on the cusp of a major transformation, driven by the emergence of LLMs like ChatGPT, PaLM, Claude, and Llama. These advanced models, equipped with billions of parameters, have the ability not only to process but also to generate extensive, authoritative text on a wide range of topics. Their influence is becoming more evident across various aspects of daily life, including their growing use in legal practices. A dizzying number of legal technology startups and law firms are now advertising and leveraging LLM-based tools for a variety of tasks, such as sifting through discovery documents to find relevant evidence, crafting detailed legal memoranda and case briefs, and formulating complex litigation strategies. LLM developers proudly claim that their models can pass the bar exam. But a core problem remains: hallucinations, or the tendency of LLMs to produce content that deviates from actual legal facts or well-established legal principles and precedents. Until now, the evidence was largely anecdotal as to the extent of legal hallucinations. Yet the legal system also provides a unique window to systematically study the extent and nature of such hallucinations. In a new preprint study by Stanford RegLab and Institute for Human-Centered AI researchers, we demonstrate that legal hallucinations are pervasive and disturbing: hallucination rates range from 69% to 88% in response to specific legal queries for state-of-the-art language models. Moreover, these models often lack self-awareness about their errors and tend to reinforce incorrect legal assumptions and beliefs. These findings raise significant concerns about the reliability of LLMs in legal contexts, underscoring the importance of careful, supervised integration of these AI technologies into legal practice. The Correlates of HallucinationHallucination rates are alarmingly high for a wide range of verifiable legal facts. Yet the unique structure of the U.S. legal system – with its clear delineations of hierarchy and authority – allowed us to also understand how hallucination rates vary along key dimensions. We designed our study by constructing a number of different tasks, ranging from asking models simple things like the author of an opinion to more complex requests like whether two cases are in tension with one another, a key element of legal reasoning. We tested more than 200,000 queries against each of GPT 3.5, Llama 2, and PaLM 2, stratifying along key dimensions. ![non thesis masters computer science in canada Bar chart showing mean hallucination rate of three language models. Llama 2 had the highest rate at 0.88](https://hai.stanford.edu/sites/default/files/inline-images/Unknown.jpeg) First, we found that performance deteriorates when dealing with more complex tasks that require a nuanced understanding of legal issues or interpretation of legal texts. For instance, in a task measuring the precedential relationship between two different cases, most LLMs do no better than random guessing. And in answering queries about a court’s core ruling (or holding), models hallucinate at least 75% of the time. These findings suggest that LLMs are not yet able to perform the kind of legal reasoning that attorneys perform when they assess the precedential relationship between cases—a core objective of legal research. Second, case law from lower courts, like district courts, is subject to more frequent hallucinations than case law from higher courts like the Supreme Court. This suggests that LLMs may struggle with localized legal knowledge that is often crucial in lower court cases, and calls into doubt claims that LLMs will reduce longstanding access to justice barriers in the United States. Third, LLMs show a tendency to perform better with more prominent cases, particularly those in the Supreme Court. Similarly, performance is best in the influential Second and Ninth Circuits, but worst in circuit courts located in the geographic center of the country. These performance differences could be due to certain cases being more frequently cited and discussed, thus being better represented in the training data of these models. Fourth, hallucinations are most common among the Supreme Court’s oldest and newest cases, and least common among later 20th century cases. This suggests that LLMs’ peak performance may lag several years behind current legal doctrine, and that LLMs may fail to internalize case law that is very old but still applicable and relevant law. Last, different models exhibit varying degrees of accuracy and biases. For example, GPT 3.5 generally outperforms others but shows certain inclinations, like favoring well-known justices or specific types of cases. When asked who authored an opinion, for instance, GPT 3.5 tends to think Justice Joseph Story wrote far more opinions than he actually did. Contrafactual BiasAnother critical danger that we unearth is model susceptibility to what we call “contra-factual bias,” namely the tendency to assume that a factual premise in a query is true, even if it is flatly wrong. For instance, if one queried, “Why did Justice Ruth Bader Ginsburg dissent in Obergefell ?” (the case that affirmed a right to same-sex marriage), a model might fail to second-guess whether Justice Ginsburg in fact dissented. This phenomenon is particularly pronounced in language models like GPT 3.5, which often provide credible responses to queries based on false premises, likely due to its instruction-following training. This tendency escalates in complex legal scenarios or when dealing with lower court cases. Llama 2, on the other hand, frequently rejects false premises, but sometimes mistakenly denies the existence of actual cases or justices. Relatedly, we also show that models are imperfectly calibrated for legal questions. Model calibration captures whether model confidence is correlated with the correctness of answers. We find some divergence across models: PaLM 2 and ChatGPT (GPT 3.5) show better calibration than Llama 2. Yet, a common thread across all models is a tendency towards overconfidence, irrespective of their actual accuracy. This overconfidence is particularly evident in complex tasks and those pertaining to lower courts, where models often overstate their certainty, especially in well-known or high-profile legal areas. Implications for the Law The implications of these findings are serious. Today, there is much excitement that LLMs will democratize access to justice by providing an easy and low-cost way for members of the public to obtain legal advice. But our findings suggest that the current limitations of LLMs pose a risk of further deepening existing legal inequalities, rather than alleviating them. Ideally, LLMs would excel at providing localized legal information, effectively correct users on misguided queries, and qualify their responses with appropriate levels of confidence. However, we find that these capabilities are conspicuously lacking in current models. Thus, the risks of using LLMs for legal research are especially high for: - Litigants in lower courts or in less prominent jurisdictions,
- Individuals seeking detailed or complex legal information,
- Users formulating questions based on incorrect premises, and
- Those uncertain about the reliability of LLM responses.
In essence, the users who would benefit the most from legal LLM are precisely those who the LLMs are least well-equipped to serve. There is also a looming risk of LLMs contributing to legal “ monoculture .” Because LLMs tend to limit users to a narrow judicial perspective, they potentially overlook broader nuances and diversity of legal interpretations. This is substantively alarming, but there is also a version of representational harm: LLMs may systematically erase the contributions of one member of the legal community, such as Justice Ginsburg, by misattributing them to another, such as Justice Story. Moving Forward with CautionMuch active technical work is ongoing to address hallucinations in LLMs. Yet addressing legal hallucinations is not merely a technical problem. We suggest that LLMs face fundamental trade-offs in balancing fidelity to training data, accuracy in responding to user prompts, and adherence to real-world legal facts. Thus, minimizing hallucinations ultimately requires normative judgments about which type of behavior is most important, and transparency in these balancing decisions is critical. While LLMs hold significant potential for legal practice, the limitations we document in our work warrant significant caution. Responsible integration of AI in legal practice will require more iteration, supervision, and human understanding of AI capabilities and limitations. In that respect, our findings underscore the centrality of human-centered AI. Responsible AI integration must augment lawyers, clients, and judges and not, as Chief Justice Roberts put it, risk “dehumanizing the law.” Matthew Dahl is a J.D./Ph.D. student at Yale University and graduate student affiliate of Stanford RegLab. Varun Magesh is a research fellow at Stanford RegLab. Mirac Suzgun is a J.D/Ph.D. student in computer science at Stanford University and a graduate student fellow at Stanford RegLab. Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law, Professor of Political Science, Professor of Computer Science (by courtesy), Senior Fellow at HAI, Senior Fellow at SIEPR, and Director of the RegLab at Stanford University. More News Topics![](//earnmoneybangla.online/777/templates/cheerup1/res/banner1.gif) |
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Program Description. The Master of Science (M.Sc.) in Computer Science (Non-Thesis) offered by the School of Computer Science in the Faculty of Science is a course-based program that emphasizes practical and rigorous learning opportunities. The program's objective is to equip students with skills in forward-thinking, data analysis, and information literacy to pursue professional opportunities.
McGill's Master of Science (M.Sc.) Computer Science (Non-thesis) aims to prepare its students for high-end industry positions involving advanced development. Students will learn about the latest developments in research and cutting edge technology in the classroom through advanced computer science courses given by the School's research ...
Program Requirements. The M.Sc. in Computer Science; Non-Thesis offers an in depth study of advanced topics in computer science, mainly through course-based work. The program includes the possibility to complete a short research project or to conduct an internship for practical experience.
This program is designed for students who want to obtain broad knowledge of advanced topics in computer science but without the requirement of a thesis. It offers an excellent preparation for the job market, but is not recommended for students interested in eventually pursuing a Ph.D. The Master of Science in Computer Science non-thesis option consists of Research Project (15 credits) - 15 ...
Complementary Courses (14 credits) At least 14 credits of COMP (or approved by MSc Graduate Program Director) courses at the 500-, 600-, or 700-level. Complementary courses must satisfy a Computer Science Breadth Requirement, with at least one course in two of the Theory, Systems, and Application areas.
9 course units in computer science (CSI) at the graduate level, including:1. 9 Units. 3 course units in Software Engineering (Category E)2. 3 course units in the Theory of Computing (Category T)2. 3 course units in Computer Applications (Category A) or in Computer Systems (Category S)2. Thesis: THM 7999.
Program Description. The Master of Science (M.Sc.) in Computer Science (Thesis) offered by the School of Computer Science in the Faculty of Science is a research-intensive program that emphasizes rigorous and cutting-edge learning opportunities. The program's objective is to equip students with skills in critical reading, forward-thinking, and academic writing to either continue their studies ...
Master's, Research: Length of program: 24 months (full-time) Registration option(s) Full-time, Part-time: Study option(s) Thesis, Master's Research Paper: Research fields - Graduate research fields are used to better define a student's research concentration. Algorithms and Complexity. Artificial Intelligence. Bioinformatics. Computer Algebra ...
Admission to the graduate programs in computer science is competitive: only the best qualified applicants are offered a seat. Therefore, it is imperative that students familiarize themselves with the admission requirements in order to ensure they submit a strong application. PhD. MSc (Thesis)
Computing and Software advances the field of computing through education and research, with a focus on computing research problems that involve scientific theory, engineering practice, and the interface between the two. We seek to address society's need for better methods of developing information systems and dependable, safe, secure, high ...
Contact the School of Graduate and Postdoctoral Studies: 905.721.8668 ext. 6209 gradstudies@ontariotechu.ca. The Computer Science master's program is broad based and covers concepts from engineering, science and business with the aim of producing high-quality software professionals.
We typically have ~150 graduate students enrolled in both M.Sc. and Ph.D. programs. The MSc in Computer Science and MSc in Applied Computing programs accept students with at least a four-year undergraduate degree, or equivalent, from any recognized college or university. These program requirements and the method of admission are described below ...
Course-based. M.Sc. 20 months. Ph.D. 4 years. The master's and doctoral programs in computer science offer students high quality, cutting-edge research opportunities and supervision by world leaders in their respective fields. Graduates of our program have gone on to work for industry leading companies such as HP Labs and Pixar Animation Studios.
The Master of Science in Computer Science (MSCS) at Northeastern University's Khoury College of Computer Sciences prepares computer science (CS) professionals in approximately two years to tackle diverse challenges and build the latest technologies. Refine your knowledge and gain expertise in three breadth areas: 1) systems and software, 2 ...
Computer science has been an active area at Lakehead University for over 25 years. The department resides in the University's Advanced Technology and Academic Centre (), a striking building that houses many of the University's computing activities.Faculty, staff, and students are equipped with state-of-the-art computing facilities including smart lecture rooms, labs, and a variety of ...
The School of Computer Science is one of the leading teaching and research centres for computer science in Canada. We offer several M.Sc. programs and a Ph.D. program, all include coursework and research. In the basic M.Sc. programs, students must choose between the thesis option, and the non-thesis option, which requires a project.
4. McGill University. The School of Computer Science is one of the leading teaching and research centers for computer science in Canada. The MSc Computer Science program at McGill includes both coursework and research. In the basic M.Sc. programs, students must choose between the thesis option, and the non-thesis option, which requires a project.
computer-science; canada; Share. Improve this question. Follow asked Jan 18, 2016 at 21:08. anon_945500 anon_945500. 313 2 2 silver ... Experience as a research assistant could provide more relevant experience than a masters thesis, particularly if the topic was appropriate.
Completing your Master's degree - course-based, non-thesis. Once you have completed all your courses and all other curriculum requirements, your program office are responsible for informing the School of Graduate Studies that you are ready to proceed to graduation. Your curriculum requirements are outlined in the School of Graduate Studies ...
About Master of Science in Computer Science. StFX offers a limited-enrolment, thesis-based MSc program in Computer Science. Learn More. Why complete an MSc at StFX? While StFX is best known for its strong tradition as a primarily undergraduate university, graduate students are a vital part of the StFX academic community.
Administrator, Graduate Studies. 519-888-4567 x42784. c3subramaniam@uwaterloo.ca. Eligibility: Must have a completed application by February 1, 2024 and be pursuing a non-thesis or professional masters full-time ( Climate Change, Geography [MRP option], Economic Development and Innovation, Development Practice, Social and Ecological ...
BBME offers a Master's of Engineering (non-thesis) with a concentration in biomanufacturing that addresses the need to enhance Canada's biomanufacturing excellence and pandemic readiness by helping to build our talent pipeline. This Master's (non-thesis) will provide students with: ♦ advanced knowledge and competencies in the rapidly evolving fields of biological and biomedical engineering ...
So, I finished my masters in computer science from a Canadian university (I'm an international student ) and bachelors in the same field in an Asian university. While my program was originally thesis based, I ended up switching to coursework based masters due to conflicting ideas with my advisor (it resulted in lots of arguments and basically ...
Hi everyone, future 2024 international masters student here, I'd like help to identify university recommendations (and dissuasions) for computer science. Found an exact thread 2 years ago but need more help. My purpose for doing a masters is to obtain PGWP (hopefully 3 years), get employed upon graduation, and then get permanent residency.
The MS in Computer Science offers both a thesis and a non-thesis option. To receive full admission to graduate study in computer science, a student must have taken intermediate-level undergraduate computer science, including computer organization, data structures, operating systems, either algorithms or theory of computation, and either compilers or survey of programming languages; and basic ...
Matthew Dahl is a J.D./Ph.D. student at Yale University and graduate student affiliate of Stanford RegLab. Varun Magesh is a research fellow at Stanford RegLab. Mirac Suzgun is a J.D/Ph.D. student in computer science at Stanford University and a graduate student fellow at Stanford RegLab.