Cornell cs vectors

Cornell cs vectors DEFAULT

The CS Major

Arts vs. Engineering Degree · Becoming a CS Major · Academic Integrity Code

General Description

Computer science majors take courses covering algorithms, data structures, logic, programming languages, systems, and theory. Electives include artificial intelligence, computer graphics, computer vision, cryptography, databases, networks, and scientific computing.

Requirements for the CS major in either the College of Arts and Sciences or the College of Engineering are as follows:

  • a calculus sequence (note different college requirements)
    • Math 1110-1120(or 1220)-2210 (A&S)
    • Math 1910-1920-2940 (ENGR or A&S)
  • Computer Science course requirements (see chart for prerequisite structure of CS courses):
  • introductory programming
    • CS 111x (CS 1110, 1112, 1114, or 1115)
    • CS 2110 (or CS 2112) or equivalent (i.e. ECE 2400/ENGRD 2140)
  • a five-course computer science core
    • CS 2800 (or CS 2802)
    • CS 3110
    • CS 3410 or CS 3420
    • CS 4410 or CS 4414
    • CS 4820
  • three 4000+ CS Electives each at three credits
    • Exceptions: CS 4090, CS 4998, and CS 4999 are NOT allowed
  • a CS Practicum or Project course:
    • CS practicums (CS 4xx1) or CS 3152, CS 4152, CS 4154, CS 4740, CS 4752, CS 5150, CS 5152, CS 5412, CS 5414, CS 5431, CS 5625, or CS 5643
  • three 3000+ Technical Electives (information) (3 credit min per course)
  • three 3000+ related courses to comprise an External Specialization--outside of computer science (3 credit min per course)
  • 3 credits Major-approved Elective(s)

For suggestions on how to select a set of electives that reflect one of a number of coherent, recognized sub-areas of study in computer science, see the material on Vectors.

In addition, students' course selections must satisfy the requirement listed below. Note that courses used to satisfy this requirement are not extra but can be incorporated into the major requirements listed above, where applicable.

  • a probability course: one of BTRY 3080, CS 4850, ECE 3100, ECON 3130, ENGRD 2700 or MATH 4710. (Choosing a 3000+ level course among these options is strongly recommended.)

Two undergraduate degrees are offered:

Neither program has a particular advantage from the standpoint of employment or graduate school.

Department Policy on Academic Integrity

Violations of the Cornell University Code of Academic Integrity occurring in Computer Science courses are taken very seriously by the Computer Science faculty. Therefore, it is necessary to impress upon students the gravity of violations of the Code. The following are excerpts from a longer version of the Cornell University Code of Academic Integrity. The exclusion of any part does not excuse ignorance of the Code.


Absolute integrity is expected of every Cornell student in all academic undertakings; he/she must in no way misrepresent his/her work fraudulently or unfairly advance his/her academic status, or be a party to another student's failure to maintain academic integrity. The maintenance of an atmosphere of academic honor and the fulfillment of the provisions of this Code are the responsibilities of the students and faculty of Cornell University. Therefore, all students and faculty members shall refrain from any action that would violate the basic principles of this Code.

General Responsibilities

  1. A student assumes responsibility for the content and integrity of the academic work he/she submits, such as papers, examinations, or reports.
  2. A student shall be guilty of violating the Code and subject to proceedings under it if he/she:
    • knowingly represents the work of others as his/her own.
    • uses or obtains unauthorized assistance in any academic work.
    • gives fraudulent assistance to another student.
    • fabricates data in support of laboratory or field work.
    • forges a signature to certify completion or approval of a course assignment.
    • in any other manner violates the principle of absolute integrity.

Specific Remarks for Students in CS Courses

Unless otherwise specified by the individual professor, the work you do in Computer Science courses is expected to be the result of your individual effort - the use of a computer in no way modifies the normal standards of the above Code. You may discuss work with other students, and give or receive "consulting" help from other students, but such permissible cooperation should never involve one student having in his or her possession a copy of all or part of another student's assignment - regardless of whether that copy is on paper, on a computer disk, or in a computer file. This implies that there is no legitimate reason to send a copy of a program from one computer account to another, or to be logged-on to another student's account.

Discussion of general strategy or algorithms is permissible, but you may not collaborate in the detailed development or actual writing of an assignment. It is also your responsibility to protect your work from unauthorized access. It is inadvisable to discard copies of your programs in public places. This applies to both hand-written and programming assignments.

The penalty for any violation of this Code in Computer Science courses may be failure in the course. This includes collaboration, providing a copy, or accepting a copy of work that is expected to be individual effort.

Computer accounts are provided for course work only. They are not private accounts; they belong to the Department of Computer Science and the use of these accounts will be monitored in various ways. Accounts that are abused will be withdrawn.


Gerard Salton

Gerard A. "Gerry" Salton (8 March 1927 in Nuremberg – 28 August 1995), was a Professor of Computer Science at Cornell University. Salton was perhaps the leading computer scientist working in the field of information retrieval during his time, and "the father of Information Retrieval".[1] His group at Cornell developed the SMART Information Retrieval System, which he initiated when he was at Harvard. It was the very first system to use the now popular vector space model for Information Retrieval.

Salton was born Gerhard Anton Sahlmann on March 8, 1927 in Nuremberg, Germany. He received a Bachelor's (1950) and Master's (1952) degree in mathematics from Brooklyn College, and a Ph.D. from Harvard in Applied Mathematics in 1958, the last of Howard Aiken's doctoral students, and taught there until 1965, when he joined Cornell University and co-founded its department of Computer Science.

Salton was perhaps most well known for developing the now widely used vector space model for Information Retrieval.[2] In this model, both documents and queries are represented as vectors of term counts, and the similarity between a document and a query is given by the cosine between the term vector and the document vector. In this paper, he also introduced TF-IDF, or term-frequency-inverse-document frequency, a model in which the score of a term in a document is the ratio of the number of terms in that document divided by the frequency of the number of documents in which that term occurs. (The concept of inverse document frequency, a measure of specificity, had been introduced in 1972 by Karen Sparck-Jones.[3]) Later in life, he became interested in automatic text summarization and analysis,[4] as well as automatic hypertext generation.[5] He published over 150 research articles and 5 books during his life.

Salton was editor-in-chief of the Communications of the ACM and the Journal of the ACM, and chaired Special Interest Group on Information Retrieval (SIGIR). He was an associate editor of the ACM Transactions on Information Systems. He was an ACM Fellow (elected 1995),[6] received an Award of Merit from the American Society for Information Science (1989), and was the first recipient of the SIGIR Award for outstanding contributions to study of Information Retrieval (1983) -- now called the Gerard Salton Award.


  • Salton, Automatic Information Organization and Retrieval, 1968.
  • Gerard Salton (1975). A Theory of Indexing. Society for Industrial and Applied Mathematics. p. 56.
  • --- and Michael J. McGill, Introduction to modern Information Retrieval, 1983. ISBN 0-07-054484-0
  • Gerard Salton (1989). Automatic Text Processing. Addison-Wesley Publishing Company. p. 530. ISBN .
  • Gerard Salton at DBLP Bibliography Server Edit this at Wikidata
  • G. Salton, A. Wong, and C. S. Yang (1975), "A Vector Space Model for Automatic Indexing," Communications of the ACM, vol. 18, nr. 11, pages 613–620. (Article in which a vector space model was presented)

See also[edit]


  1. ^ ab"The father of Information Retrieval"(PDF). Retrieved 10 March 2015.
  2. ^Salton, G.; Wong, A.; Yang, C. S. (1975). "A vector space model for automatic indexing". Communications of the ACM. 18 (11): 613. doi:10.1145/361219.361220. hdl:1813/6057. S2CID 6473756.
  3. ^Spärck Jones, K. (1972). "A Statistical Interpretation of Term Specificity and Its Application in Retrieval". Journal of Documentation. 28: 11–21. CiteSeerX doi:10.1108/eb026526.
  4. ^Salton, G.; Allan, J.; Buckley, C.; Singhal, A. (1994). "Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts". Science. 264 (5164): 1421–1426. Bibcode:1994Sci...264.1421S. doi:10.1126/science.264.5164.1421. PMID 17838425. S2CID 32296317.
  5. ^"Gerard Salton". Retrieved 2013-09-14.
  6. ^"Gerard Salton ACM Fellows 1995". Retrieved 10 March 2015.

External links[edit]

  • In Memoriam
  • Fractals of Change: Search Down Memory Lane
  • The Most Influential Paper Gerard Salton Never Wrote - This 2004 Library Trends paper by David Dubin serves as a historical review of the metamorphosis of the term discrimination value model (TDV) into the vector space model as an information retrieval model (VSM as an IR model). This paper calls into question what the Information Retrieval research community believed Salton's vector space model was originally intended to model. What much later became an information retrieval model was originally a data-centric mathematical–computational model used as an explanatory device. In addition, Dubin's paper points out that a 1975 Salton paper oft cited does not exist but is probably a combination of two other papers, neither of which actually refers to the VSM as an IR model.
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Choosing a Coherent Set of Electives in CS

Choosing a Coherent Set of Electives

The grade point average is but one way to measure the quality of an undergraduate record. An equally important attribute concerns depth-of-education, something that is best measured by the choice of elective courses. While required core CS courses ensure breadth-of-education and set the stage for more specialized work, it is through the choice of electives that you can communicate an appealing level of curiosity and a readiness for the future.

With this in mind, the faculty have developed a collection of vectors that promote coherent, in-depth study.  Each vector has a magnitude (typically four to five courses, which may be internal or external to CS) and a direction representing a particular "line of inquiry" within CS.

The faculty expect the set of vectors to evolve over time and plan to introduce or modify vectors as they see new directions for the department and the field.

Pursuing a vector (or one of its more specialized tracks) is optional and not a requirement for graduation. It should be driven by academic interest alone rather than based on risky predictions of where the field is going. The best way to ensure a successful career after you graduate as a CS major is to practice now the art of being intellectually nimble. Tumultuous times call for mobility. Cultivate a broad knowledge of the field and show the world through your transcript that it empowers you to go deep and to learn new areas. Choose your electives as if your life depended on it. It does.

Vector Descriptions 

NOTE: The "F" notation Fxxx (or variants like F7xx) is used, informally speaking, to denote an Four or Five or, um, Six-thousand level Full-Fledged elective. It does not mean a 4000-level course (otherwise, we would have written "4" instead of "F".) Specifically and technically, it stands for any course that's at the 4000-6000 level, at least 3 credits, not an independent study or teaching for credit, and not required by the CS major. For example, Fxxx includes 4700, 5620, and 6110, but not 4820 (required by the major), 4090 (teaching experience in CS), 4999 (independent study), or 4121 (fewer than 3 credits). As another example, CS F4xx includes CS 4450 and 5430, but not CS 4410 (required by the major), 4411 (too few credits) or 4700 (doesn't match the pattern!).


Focuses on understanding and creating intelligent systems.

Explanation of requirements: the core AI course and associated practicum, machine learning, an AI elective, and either connections to human intelligence or further grounding in techniques.

Required courses: Five total (4 full and 1 half)

  1. CS 4700, Foundations of Artificial Intelligence
  2. CS 4701, Practicum in Artificial Intelligence
  3. An additional course numbered CS F78x;
  4. An additional course numbered CS F7xx or CS 4300 (Language and Information) or CS F67x (Computer Vision) or CS 5846 (Decision Theory);
  5. An additional course chosen from the following list: COGST, PSYCH or LING Fxxx, CS F2xx, CS 32xx. (Recall that independent studies such as LING 4493 or courses below 3 credits are disallowed according to the Fxxx naming convention.)

Also recommended are (additional) courses on scientific computing (e.g.,  CS 4210 or 4220), game theory or related economics topics.

Track: Human-Language Technology 

Focuses on creating natural-language processing and information retrieval systems that can analyze and generate information in unstructured, natural-language format (e.g., English). Applications include search engines, automatic translations between languages, and human-computer interaction, among others.

In addition to core AI and machine learning, the track focuses on courses covering the fundamentals of computational linguistics, natural language processing and/or information retrieval, and linguistics. The track also adds another course from those areas or from programming languages. In addition to the vector requirements:

  • the course taken under (4) is either CS F74x or CS 4300
  • the course taken under (5) is LING Fxxx (e.g. LING 4485)
  • students take one additional course LING Fxxx (includes CS 3470) or CS F74x or CS F110

Also recommended are CS 2043 (UNIX Tools and Scripting), statistics, scientific computing (courses number x2xx), a foreign language.

Track: Machine Learning

Focuses on machine learning methods and theory, with their numerous applications to large-scale data analysis problems in areas like search engines, natural language processing, data mining, robotics, and computational biology.

In addition to the core AI course and the first machine learning course, the track includes a second course in machine learning or statistics. It also includes a course in an application area of machine learning or in an area that machine learning draws upon. In addition to the vector requirements (1)-(3), students take the following two courses which might count under (4) and (5), only if permitted by the AI vector:

  • another course from CS F78x or CS 4850 (Math Found for the Info Age) or STSCI Fxxx (excluding STSCI 4080).
  • one course from CS 4300 (Language and Technology) or CS F74x (Natural Language Processing) or CS F75x (Robotics) or CS F67x (Computer Vision) or CS F2xx (Scientific Computing).


Focuses on the numerical algorithms that support computer modeling and simulation to guide experimental and design work in data-intensive scientific computing.

Explanation of requirements: Effective numerical computing in science and engineering requires an understanding of the fundamental algorithms that have been devised to solve the continuous problems of applied mathematics. Requirements include a year-long sequence in numerical analysis that covers data fitting and approximation, nonlinear systems and optimization, ordinary and partial differential equations, and the essential methods of numerical linear algebra. An applied mathematics course that covers differential equations and/or optimization together with a programming versatility requirement completes the vector.

Required courses: Five minimum (3 full and 2 or more half). Note that a prerequisite for this vector is MATH 2930 (Differential Equations for Engineers) or Math 2220 (Multivariable Calculus).

  1. Two (different) courses numbered F2xx, with the following exception: one cannot count both CS 4220 (Numerical Analysis II: Linear and Nonlinear Problems) and CS 6210 (Matrix Computations), due to overlapping content. 

    Note that one can take CS 4210 (Numerical Analysis I: Numerical Analysis and Differential Equations) and CS 4220 in either order.
  2. One of the following applied mathematics courses: OR 3300 (Optimization I), TAM 3100 (Applied Mathematics I), Math 4200 (Differential Equations and Dynamical Systems), Math 4240 (Wavelets and Fourier Series), Math 4280 (Introduction to Partial Differential Equations), AEP 4210 (Mathematical Physics I), CEE 3310 (Fluid Mechanics), CEE 3710 (Structural Modeling and Behavior), MAE 3230 (Introductory Fluid Mechanics). 
  3. A minimum of four-credit hours of programming experience obtained through any combination of CS 20xx courses: CS 2022 (Introduction to C), CS 2024 (C++ Programming), and CS 2043 (UNIX Tools and Scripting). 

Also recommended are CS 3300 (data-driven web applications), CS 4320 (introduction to database systems), computer graphics courses with significant mathematical content numbered (typically numbered CS F6xx or corresponding cross-lists), CS F78x (machine learning courses).


Focuses on computing with visual images

Explanation of requirements: The introductory graphics or vision course and practicum, scientific computing, and two electives (in various combinations) in graphics and related fields

Required courses: Five total (4 full and 1 half)

  1. CS 4620, introduction to computer graphics or CS 4670, introduction to computer vision
  2. CS 4621, computer graphics practicum
  3. CS 32xx or F2xx (scientific computing)
  4. An additional course numbered CS F6xx  (graphics and vision courses)
  5. An additional course drawn from the following list: CS F6xx  (graphics and vision courses) or CS 3152 or CS 4152 (game design courses) or CS F78x (machine learning)

Also recommended are courses in visual art or photography or human perception especially as applied to computer graphics/art/visual display (e.g. PSYCH 3420), as well as additional 4000-level mathematics courses.


The social, technological, and natural worlds are connected; the study of networks sheds light on these connections. This vector provides preparation in the network- and data-analysis tools to understand and develop predictive models of physical, biological, and social network phenomena.

Explanation of requirements: Two core courses in the analysis of networks; a course in machine learning; and a course on application areas

Required courses: Four total (4 full and 0 half)

  1. Two different courses numbered CS x85x (complex networks/information access), or INFO 4220 (Networks II). To be clear: CS 2850 counts towards this requirement.
  2. One advanced technical course dealing with understanding data. Courses that count towards this requirement are coursees numbered CS F78x (machine learning courses) or CS 4758 (Robot Learning) or CS 4740 (Natural Language Processing) or CS 3300 (Data-driven Web Applications) or CS 4300 (Language and Information).
  3. One course chosen from the following list: ORIE x350 (Game Theory), ECON 4020 (Game Theory; formerly, ECON 3680), CS F84x (game theory courses), or INFO 4220 (Networks II) if not already used to satisfy the requirements above.


Spans logics and language semantics, language design, compilation and optimization.

Explanation of requirements: Requirements include a course on programming languages; a course on compilers and the associated practicum; an elective in either applied logic, computability and complexity, or advanced programming languages; and a (short) course on a particular language.

Required courses: Five total (3 full and 2 half)

  1. CS F110 (programming-language and logics courses)
  2. CS 4120 (introduction to compilers)
  3. CS 4121 (practicum in compilers)
  4. An additional course chosen from the following list: CS 4860 (applied logic), CS 5114 (network programming languages), CS 5860 (intro to formal methods), CS F810 (computability and complexity), or CS 611x if not already used to satisfy the requirements above.
  5. One short course, CS 202x, on an additional programming language. For example, 2022 (C), 2024 (C++) qualify.

Since 4110 and 4120 are typically offered in alternate years, completion of this vector usually requires taking one of these courses by fall of one's junior year.


Provides students with the fundamentals of software engineering and extensive implementation experience in a variety of application areas.

Explanation of requirements: a masters-level course on software engineering, two programming-intensive practica in different areas, and an additional course with a heavy implementation component. Experience in C++ is strongly recommended (note that many CS majors are exposed to Linux through the core course CS 3410). 

Required courses: Four total (2 full and 2 half, but at least one additional full course corresponding to one of the half-course practica is strongly recommended (and in some cases required))

  1. CS 5150 (Software Engineering) or CS 5152 (Open_source Software Engineering)
  2. Two different courses numbered 4xx1 (practicums, such as 4121 (compilers), 4321 (databases), 4411 (operating systems), 4621 (computer graphics), 4701 (artificial intelligence)). 
    Note: It is strongly recommended (and in some cases required) that one also take the corresponding "full" course; for example, if one takes 4621 (the graphics practicum), it is recommended that one also take 4620 (introduction to computer graphics)
  3. One of CS 3152 (Introduction to Computer Game Development), CS 4152 (Advanced Topics in Computer Game Architecture), CS 4154 (Analytics-Driven Game Design), CS F45x (computer networks), CS 5412 (Cloud Computing), 5414 (Distributed Computing Systems), or the following implementation-heavy computer graphics courses: CS 5625.

Also recommended are exposure to C++, either through 2024 (C++ programming) or practica taught in C++; 2043 (UNIX tools and Scripting); CS 2300 (intermediate design and programming for the web), CS 3300 (data-driven web applications).


Focuses on design and implementation of the fundamental software systems that comprise our computing infrastructure.

Explanation of requirements: the core operating-systems practicum, and three systems electives (areas include networking, architecture, and so on). The launch-point for this vector is CS4410 (operating systems, a core CS course) and its practicum, CS 4411. Many students follow on with CS4450/5450 (networks, not offered every year), CS 5430 (security) and/or CS 5412/14 (intermediate computer systems: cloud and distributed computing and high performance systems and networking), and other 54xx courses). Note: undergraduates should not have difficulty taking these CS 54xx courses provided that they had no difficulty in CS4410/CS4411.

Required courses: Four total (3 full, 1 half)

  1. CS 4411 (Practicum in Operating Systems) or CS 4321 (Practicum in Database Systems)
  2. 3 additional courses chosen from the following list: CS F4xx, CS F12x (compilers courses), CS F32x (database courses), and ECE 4450 (computer networks and telecommunications)

Although any group of courses from the acceptable list will do, many systems students find it especially useful to get a solid background in databases.

Given the definition of "Fxxx" from above, CS F4xx includes CS 4450/5450, CS 5430, CS 5412, CS 5413, and CS 5414, but does not include CS4410 or CS 4411; similarly, CS 4321 is not included under F32x, and CS 4121 is not included under CS F12x.

Track: Operating Systems 

Provides detailed and hands-on knowledge of operating systems and low-level systems software.

In order to build a detailed understanding of operating systems and low-level systems software, the following course is required in addition to the vector requirements:

  • the practicum under (1) is CS 4411 (Practicum in Operating Systems).

Track: Security & Trustworthy Systems 

Addresses fundamental problems in ensuring the security and reliability of our global critical computing infrastructure.

Combines operating systems with mathematical tools for cryptography or verification, system security, and the second course in the systems core sequence. In addition to the vector requirements:

  • the practicum under (1) is CS 4411 (Practicum in Operating Systems).
  • one course under (2) is CS 5430 (System Security)
  • one course under (2) is CS 5412 (Cloud Computing) or 5414 (Distributed Computing Systems).
  • students take one additional course CS 4830 (Introduction to Cryptography) [preferred] or CS4860 (Applied Logic) or MATH 3360 (Applicable Algebra)

Also recommended are INFO 5150 (Culture, Law and Politics of the Internet) or other relevant law or policy courses. Note: undergraduates should not have difficulty taking the CS 54xx courses provided that they had no difficulty in CS 4410/CS 4411.

Track: Data-Intensive Computing 

Focuses on the foundations of managing, processing, and analyzing large datasets

Combines one course plus practicum on data management and courses on large data-driven systems, with machine learning/data mining or information retrieval. In addition to the vector requirements:

  • the practicum under (1) is CS 4321 (Practicum in Database Systems);
  • one of the three courses under (2) isCS 4320 (Database Systems);
  • students take one additional course from CS F78x (Machine Learning) or ORIE 4740 or CS 4300 (Language and Technology) or CS 4740 (Natural Language Processing)

Vector name: THEORY

Focuses on the study of efficient computation, models of computational processes, and their limits.

Explanation of requirements: a course in computability/complexity, two theory electives and an upper-level technical mathematics course 

Required courses: Four total (4 full, 0 half)

  1. CS 481x (intro theory course)
  2. Two additional courses numbered CS F8xx or from the following list: ECE 6890 (An Algorithmic and Information-Theoretic Toolbox for Massive Data), ORIE 6330 (Graph Theory and Network Flow), ORIE 6335 (Scheduling Theory), ORIE 6334 (Combinatorial Optimization). (Recall that CS 4820 does not satisfy this requirement, since the "F" notation indicates a 4000-6000 non-CS-major-required 3+ credit course.)
  3. An additional 3+ credit course numbered MATH THxx, where the thousands digit T is between 3 and 6 inclusive and where the hundredths digit is between 1 and 9 inclusive, or MATH 4010 (Honors seminar: Topics in Modern Mathematics) or CS 4860 (Applied Logic).

Also recommended are CS 4210 or 4220 (Scientific computation, numerical analysis); MATH 4320 or 4340 (Abstract algebra); MATH 4810 (Logic).

Cornell CS 5787: Applied Machine Learning. Lecture 7. Part 1: Generative Models

Is Cornell the right place for a CS focused person like me?

Hi! I’m a sophomore CS student in the College of Engineering. Hope I can answer some of your questions and concerns…

“In College I want to explore Computer Science as much as possible. Cornell’s “vectors” program is brilliant. Unfortunately as near as I can tell, you only get through 1 vector during your undergraduate. I would love to get through 2 or 3.”

->There’s a Renaissance vector which lets you take basically any combination of CS courses you want. The vector doesn’t limit your scope, so don’t worry. You can also do more than 1 vector; on the application to affiliate (undergrads fill it out by junior year), you can list up to three possible vectors you want to take.

“I want to explore computer graphics, cyber security, and machine learning/ai as much as possible. While Cornell offers a bunch of classes on these topics I am not sure I will be able to explore all of them. Furthermore, I am worried that I will be ridiculed or discouraged by the staff to do so. Cornell seems to be really proud of being interdisciplinary.”

->Doesn’t seem like something I’d discourage. Go for it! Cyber security is a little hard to get into quickly. For a freshman with no CS knowledge coming in, he/she would have to take CS 1110/1112, then CS 2110, then CS 3410, then CS 4410, and then start taking cyber security classes. That’s two semesters at the maximum if you do one class after the other (each one is a prereq for the next); there are other core classes required, like CS 2800, but you can take it anytime after taking 1110/1112. Computer graphics and machine learning are courses alot of CS undergrads take; I plan to take my first graphics course (CS 4620) this semester.

“A bunch of the praises I hear about Cornell is how they have so many majors and so you can easily switch or come in now knowing what you want to study. I also hear that the school is big on having you take seemingly random classes outside your major. I am fine with that but when it becomes 2/3rds of your curriculum it is a bit ridiculous.”

->CS is so intensive that even if it takes up 40% of my classes for the semester, it accounts for 75% of the workload. Plus, I like to take other classes related to my other interests but also related to CS–for example, Math and philosophy. This way I am learning a variety of concepts I can apply to CS and am delving into topics (seemingly) unrelated to CS that enhance my education/

“Compound this on my disdain for Cornell’s banning of mistletoe. Does Cornell have policies that extreme all the time? Does diversity to them just mean muted versions of all cultural practices or perhaps just English ones? I am a bit conservative will I fit in?”

->Cornell is a very liberal campus, but freedom of speech, thought, religion etc is respected. I think many of the students at Cornell acknowledge the other side and don’t promote censorship–since hearing the other side of the argument is necessary for wholesome truth. But, I won’t lie in saying that there are students who are extreme on both sides. This spread is typical in a lot of college campuses. As long as you respect others, you will be respected.

“I love the campus, I love the course catalog, I love the research, I love descriptions of the people. But is Cornell still a school I should apply to?”

->YES. I love it here. The people are great and the classes are very interesting.

“PS: I also LOATHE Cornell notes. I am not sure if they have anything to do with the school but so help me if another teacher tries to force me to take notes their way.”

Don’t worry. No one I know has even heard of Cornell notes. Takes notes however you want.


Cs vectors cornell


Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)


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