CS 208E, Great Ideas in Computer Science

This project-based class focuses on the design of social computing and crowdsourcing systems. Students will learn how to engage large groups of people online, from microtask crowdsourcing to the design of online communities. The course will cover best practices for system design such as motivating participation, ethical guidelines, agreement measures, and gold standards. Advanced topics such as expert and team-based crowdsourcing, incentive design, and complex crowd workflows will also be discussed. Students will learn about the application of crowdsourcing to other areas of computer science, and how the field relates to social psychology and organizational behavior. Prerequisite: .

CS 54N. Great Ideas in Computer Science. 3 Units.

CS 198B. Additional Topics in Teaching Computer Science. 1 Unit.

CS 208E. Great Ideas in Computer Science. 3 Units.

Pre-requisites: Basic probability, linear algebra, computer programming, and graduate or undergraduate senior standing, OR approval of instructor. This course is an introduction to machine learning and contains both theory and applications. Students will get exposure to a broad range of machine learning methods and hands on practice on real data. Topics include Bayesian classification, perceptron, neural networks, logistic regression, support vector machines, decision trees, random forests, boosting, dimensionality reduction, unsupervised learning, regression, and learning new feature spaces. There will be several programming assignments, one course project, one mid-term and one final exam.

CS 359. Topics in the Theory of Computation. 3 Units.

For those with limited experience with computers or who want to learn more about Stanford's computing environment. Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by the Resident Computer Consultant (RCC). Final project. Not a programming course.

CS 359C. Topics in Theory of Computation: Classics of Cryptography. 3 Units.

What is the difference between a thesis vs

Prerequisite: CS 540 or equivalent. Review of basic computability theory. Topics include Church's thesis; unsolvability results; creative, productive, and simple sets; computational complexity; P=NP problem; and classification of solvable problems according to their complexity.

Non thesis for MS in Materials Science Engineering

A candidate is required to complete a program of 45 units. At least 36 of these must be graded units, passed with a grade point average (GPA) of 3.0 (B) or better. The 45 units may include no more than 10 units of courses from those listed below in Requirement 1. Thus, students needing to take more than two of the courses listed in Requirement 1 actually complete more than 45 units of course work in the program. Only well-prepared students may expect to finish the program in one year; most students complete the program in six quarters. Students hoping to complete the program with 45 units should already have a substantial background in computer science, including course work or experience equivalent to all of Requirement 1 and some prior course work related to their specialization area.

The following are general department requirements. Contact the Computer Science Ph.D. administrator for details.

transition words for essay Thesis Vs Non Thesis Master Degree ..

We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of .
Same as: MS&E 317

What about MS non thesis degree with individual research publications ? Will it create any problem for pursuing PhD ?

Non Thesis Masters Degree+Computer Science - …

Prerequisites: Knowledge of material from at least four courses in the following list: (Data Management Systems Design), (Data Mining), (Cloud Computing), (Introduction to Big Data), (Machine Learning).Targeting the latest computing infrastructures and software systems for data analytics, this course introduces students to the design and analysis of scalable data science algorithms, as well as skills to implement high performance data science applications. Specific topics include in-memory data processing, column-oriented data storage and retrieval, cloud-based data intensive systems, as well as classic data analytics algorithms such as causal discovery and network inference and their scalable implementation.

Completion of the undergraduate program in Computer Science leads to the conferral of the Bachelor of Science in Computer Science.

Master thesis topics in computer science focus papers in ..

This course presents a graduate introduction to Medical Informatics for Computer Science students covering (1) the design, use and auditing of medical terminologies, such as the Unified Medical Language System (UMLS) and the Systematized Nomenclature of Medicine (SNOMED); and (2) principles of Electronic Medical Records (EMR), Electronic Health Records (EHR) and Personal Health Records (PHR), including issues of privacy and security.