AI News, Computer Science Undergraduate Major

University Bulletin (2019-2020)

Topics include problem solving, planning natural language processing, knowledge representation, and computer vision.

Prerequisite: COSI 21a and either COSI 166b or permission of the instructor.Covers some of the 'big ideas' that come in to play when building large, complex, and highly scaled software systems.

And we apply this learning working in teams of students to design and implement their own version of the Twitter backend from the ground up, and then stress test and measure it’s scalability using real world tools and technologies.

Prerequisite: COSI 101a or 125a, or permission of the instructor.Focuses on cognitive and social theories of activity that underlie human computer interaction and computer mediated collaboration.

Topics include problem solving and skill acquisition, planning and situated activity, distributed cognition, activity theory, collaboration, communication, discourse, and interaction analysis.

The laboratory work is designed to give the student practice with the ideas and techniques under discussion.

The content and work of the course are specifically designed for an interdisciplinary class of students from computer science and the social sciences.

This includes (briefly) propositional logic and first order logic, and then an in-depth study of modal logic, temporal logic, spatial logic, and dynamic logic.

Prerequisite: COSI 21a.Explores genetic algorithms, genetic programming, evolutionary programming, blind watchmaking, and related topics, ultimately focusing on co-evolutionary spirals and the automatic construction of agents with complex strategies for games.

Prerequisites: COSI 11a and LING 131a, or permission of the instructor.Provides a fundamental understanding of the problems in natural language understanding by computers, and the theory and practice of current computational linguistic systems.

Of interest to students of artificial intelligence, algorithms, and the computational processes of comprehension and understanding.

Since voice is the most natural medium for human communication, spoken dialog is becoming an essential part of the interface.

It requires knowledge from many disciplines including linguistics, artificial intelligence, computer-human interaction, and computational linguistics.

This course will bring together the essential elements of these fields and the software skills and tools required to build an effective dialog system and guide students through hands-on projects applying that knowledge to real applications.

Laboratory work enables the student to practice a set of basic techniques as they apply to the development of computer-mediated collaboration.

The content and work of the course are specifically designed for an interdisciplinary class of students from computer science and the social sciences.

May not be taken for credit by students who took COSI 21b in prior years.An introduction to idioms of programming methodology, and to how programming languages work.

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Prerequisite: COSI 29a and MATH 10a.Focuses on learning from data using statistical analysis tools and deals with the issues of designing algorithms and systems that automatically improve with experience.

This course is designed to give students a thorough grounding in the methodologies, technologies, mathematics, and algorithms currently needed by research in learning with data.

Topics include methodology for designing and testing user interfaces, interaction styles and techniques, design guidelines, and adaptive systems.

The laboratory work is designed to give the student practice in a set of basic techniques used in the area of human-computer interaction.

Prerequisites: COSI 12b and COSI 21a, MATH 15a or MATH 22a.Introduces the basic concepts, principles, methods, and implement techniques of data mining for large-scale and big data analysis, with three data mining tasks: clustering, classification and association mining.

Some basic concepts, tasks, relationships with statistics and machine learning are provided including unsupervised, supervised and semi-serviced learning.

Studies relational and object-oriented models, query languages, optimization, normalization, file structures and indexes, concurrency control and recovery algorithms, and distributed databases.

Prerequisite: COSI 12b.Provides the knowledge to use Big Data tools and learn ways of collecting, processing and analyzing massive amounts of unstructured data.

This is an interdisciplinary course that will combine techniques from system design, distributed processing, machine learning and natural language processing to address big data analytic tasks.

May not be taken for credit by students who took COSI 30a in prior years.Formal treatment of models of computation: finite automata and regular languages, pushdown automata and context-free languages, Turing machines, and recursive enumerability.

Fundamental structures of a computer system from hardware abstractions through machine and assembly language, to the overall structure of an operating system and key resource management abstractions.

Prerequisite: COSI 21a, COSI 101a or COSI 114b.Explores the theory and practice of textual information retrieval, including text indexing;

Prerequisite: COSI 131a.Explores the fundamental concepts in design and implementation of networked information systems, with an emphasis on data management.

In addition to distributed information systems, we will also study modern applications involving the web, cloud computing, peer-to-peer systems, etc.

This course may be taken concurrently with COSI 114b.An introductory graduate-level course covering fundamental concepts in statistical Natural Language Processing (NLP).

Provides an in-depth view of the statistical models and machine-learning methods used in NLP, including methods used in morphological, syntactic, and semantic analysis.

Open to advanced undergraduate students and first-year graduate students.A study of the computational treatment of core semantic phenomena in language.

After a review of first-order logic and the lambda calculus, the course focuses on three core topics: interrogative structures, including semantics of questions, question-answering systems, dialogue, entailment, commonsense knowledge;

Covers phonetics, HMMs, finite state grammars, statistical language models, and industry standards for implementing applications, like VXML.

Prerequisites: COSI 101a, COSI 121b, COSI 134a or permission of the instructor.Examines the major issues and techniques in extracting semantically meaningful information from unstructured data, putting the information into a structured database for easy access and manipulation.

Students in Computer Science MA or PhD programs may enroll with permission of the instructor.A seminar on research methods, writing, and presentations, and in abstract writing.

Aims to help students learn to prepare and deliver oral presentations and written papers of their research work, according to the standards used and expected in this field—both in industry job and academic settings.

The field of the learning sciences combines cutting edge cognitive research on learning and teaching with technological innovation.

In this class students develop their skill and practice for design-based technological innovation as it applies to learning and education.

Prerequisite: COSI 114b or LING 131a and concurrent enrollment in COSI 114b.Studies corpus linguistics, the computational study of any naturally occurring fragment of language, a key area for data mining, information extraction, and machine learning.

Students model, annotate, train, test, evaluate, and revise their own corpus for machine learning.

Prerequisites: COSI 131a and MATH 10a (MATH 10b recommended).Topics on the design and engineering of computer systems: techniques for controlling complexity;

Case studies of working systems and readings from the current literature provide comparisons and contrast.

Prerequisite: COSI 131a and C/C++/UNIX programming skills.This course covers abstractions and implementation techniques for the design of distributed systems.

Topics include: distributed state sharing, coherence, storage systems, naming systems, security, faulttolerance and replication, scalability and performance.

Prerequisite: COSI 12b.In this experiential learning course, students will learn and practice machine learning techniques (such as regression, clustering, decision trees, support vector machines, assemble techniques, and deep-learning) to tackle real problems in industry and/or interdisciplinary research.

Prerequisites: COSI 11a, 12b, or permission of the instructor.An introduction to web programming that covers the fundamental languages and tools, including HTML/CSS for page layout, javascript/ajax for client-side integration, and server-side programming in javascript using Node/Express, Mongo, and MySQL.

Prerequisites: COSI 11a and 12b.Introduces the design and analysis of mobile applications that covers the architecture of mobile devices, APIs for graphical user interfaces on mobile devices, location-aware computing, social networking.

Also covers the theory and practice of space and time optimization for these relatively small and slow devices.

Covers agile programming techniques, rapid prototyping, source control paradigms, effective software documentation, design of effective APIs, software testing and analysis, software licensing, with an introduction to business plans for software entrepreneurs.

An introduction to the art of displaying computer-generated images and to the design of graphical user interfaces.

May not be taken for credit by students who took COSI 65a in prior years.Covers the fundamental concepts of 3-D animation and teaches both the theory underlying 3-D animation as well as the skills needed to create 3-D movies.

Prerequisite: Sophomore standing.Covers the fundamental concepts needed to transform an idea for a software application into a viable IT business.

Teaches modern software engineering concepts, emphasizing rapid prototyping, unit testing, usability testing, and collaborative software development principles.

Students apply these concepts by building a complex software system in small teams of programmers/developers using current platforms and technologies.

A seminar studying research papers relating to data compression and information extraction / understanding from multimedia, including content based image and video retrieval, object recognition, data analytics, and related applications of machine learning.

Programming concepts such as data types, vectors, conditional execution, loops, procedural abstraction, modules, APIs are presented.

The course will present scientific techniques relevant to computational science, with an emphasis on image processing.

Open to advanced undergraduate students and graduate students.Information and computing technologies are becoming indispensable to modern biological research due to significant advances of high-throughput experimental technologies in recent years.

This course presents an overview of the systemic development and application of computing systems and computational algorithms/techniques to the analysis of biological data, such as sequences, gene expression, protein expression, and biological networks.

Prerequisite: COSI 121b or familiarity with a functional programming language, set theory and logic.An introduction to the mathematical semantics of functional programming languages.

simply typed lambda calculus and its model theory: completeness for the full type frame, Statman's 1-section theorem and completeness of beta-eta reasoning;

linear logic, proofnets, context semantics and geometry of interaction, game semantics, and full abstraction.

Undergraduate Studies

degree in Computer Science can specialize in the area of Cybersecurity by successfully completing the following three technical elective courses to earn a Bachelor of Science (B.S.) in Computer Science with a concentration in cybersecurity: For students with bulletin years through 2018-2019 Students enrolled in the B.S.

degree in Computer Science programs can specialize in the area of Data Science by successfully completing the following three of the following technical elective courses to earn a Bachelor of Science (B.S.) in Computer Science with a concentration in data science:

Andrew Ng

He launched and heads AI Fund, a $175 million investment fund to back artificial intelligence startups.

In 1997, he received his undergraduate degree with a triple major in computer science, statistics, and economics at the top of his class from Carnegie Mellon University in Pittsburgh, Pennsylvania.

He became Director of the Stanford Artificial Intelligence Lab, where he taught students and undertook research related to data mining and machine learning.

His deep learning course at Stanford is the most popular course offered on campus with over 1000 students enrolled.[6]

In 2012, he co-founded and was CEO of Coursera which offers free online courses for everyone after over 100,000 students registered for Ng's popular course.[7]

Ng researches primarily in machine learning and deep learning and is one of the world's most famous computer scientists.[18]

In 2011, Ng founded the Google Brain project at Google, which developed large scale artificial neural networks using Google's distributed computer infrastructure.[22]

Among its notable results was a neural network trained using deep learning algorithms on 16,000 CPU cores, which learned to recognize cats after watching only YouTube videos, and without ever having been told what a 'cat' is.[23][24]

The 'applied' version of the Stanford class (CS229a) was hosted on ml-class.org and started in October 2011, with over 100,000 students registered for its first iteration;

Ng announced a new course 'AI for Everyone' which will be available on Coursera in early 2019 to help people understand AI's impact on society and how companies can navigate through this technological change.[31]

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