Computer science graduate programs in computer

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Computer Science

1

COMPUTER SCIENCE

Courses offered by the Department of Computer Science are listed under the subject code CS on the Stanford Bulletin's ExploreCourses (http:// explorecourses.stanford.edu/CourseSearch/search?view=catalog&/ #38;catalog=&) web site.

The Department of Computer Science (CS) operates and supports computing facilities for departmental education, research, and administration needs. Current CS students have access to a departmental student machine for general use and computer labs located in the Gates Building. In addition, most students have access to systems located in their research areas.

Each research group in Computer Science has systems specific to its research needs. These systems include workstations, computer clusters, GPU clusters, and local file servers. Servers and workstations running Linux , MacOS, or various versions of Windows are commonplace. Support for course work and instruction is provided on systems available through U ()niversity IT () (UIT) and the School of Engineering (http:// engineering.stanford.edu/) (SoE).

Mission of the Undergraduate Program in Computer Science

The mission of the undergraduate program in Computer Science is to develop students' breadth of knowledge across the subject areas of computer science, including their ability to apply the defining processes of computer science theory, abstraction, design, and implementation to solve problems in the discipline. Students take a set of core courses. After learning the essential programming techniques and the mathematical foundations of computer science, students take courses in areas such as programming techniques, automata and complexity theory, systems programming, computer architecture, analysis of algorithms, artificial intelligence, and applications. The program prepares students for careers in government, law, and the corporate sector, and for graduate study.

Learning Outcomes (Undergraduate)

The department expects undergraduate majors in the program to be able to demonstrate the following learning outcomes. These learning outcomes are used in evaluating students and the department's undergraduate program. Students are expected to be able to:

1. Apply the knowledge of mathematics, science, and engineering. 2. Design and conduct experiments, as well to analyze and interpret

data. 3. Design a system, component, or process to meet desired needs

within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability. 4. Function on multidisciplinary teams. 5. Identify, formulate, and solve engineering problems. 6. Understand professional and ethical responsibility. 7. Communicate effectively. 8. Understand the impact of engineering solutions in a global, economic, environmental, and societal context. 9. Demonstrate a working knowledge of contemporary issues. 10. Apply the techniques, skills, and modern engineering tools necessary for engineering practice. 11. Transition from engineering concepts and theory to real engineering applications.

Learning Outcomes (Graduate)

The purpose of the master's program is to provide students with the knowledge and skills necessary for a professional career or doctoral studies. This is done through course work in the foundational elements of the field and in at least one graduate specialization. Areas of specialization include artificial intelligence, biocomputation, computer and network security, human-computer interaction, information management and analytics, real-world computing, software theory, systems, and theoretical computer science.

The Ph.D. is conferred upon candidates who have demonstrated substantial scholarship and the ability to conduct independent research. Through course work and guided research, the program prepares students to make original contributions in Computer Science and related fields.

Graduate Programs in Computer Science

The University's basic requirements for the M.S. and Ph.D. degrees are discussed in the 'Graduate Degrees ( graduatedegrees/)' section of this bulletin.

Computer Science Course Catalog Numbering System

The first digit of a CS course number indicates its general level of sophistication:

Digit 001-099

100-199

200-299

300-399 400-499 500-599

Description Service courses for nontechnical majors Other service courses, basic undergraduate Advanced undergraduate/beginning graduate Advanced graduate Experimental Graduate seminars

The tens digit indicates the area of Computer Science it addresses:

Digit 00-09 10-19 20-39 40-49 50-59

60-69 70-79

90-99

Description Introductory, miscellaneous Hardware and Software Systems Artificial Intelligence Software Systems Mathematical Foundations of Computing Analysis of Algorithms Computational Biology and Interdisciplinary Topics Independent Study and Practicum

Bachelor of Science in Computer Science

The department offers both a major in Computer Science and a minor in Computer Science. Further information is available in the Handbook for Undergraduate Engineering Programs (UGHB) () published by the School of Engineering. The Computer Science major offers a number of tracks (programs of study) from which students can choose, allowing them to focus their program on the areas of most interest. These tracks also reflect the broad diversity of areas in computing disciplines. The department has an honors program.

Stanford Bulletin 2019-20

2 Computer Science

In addition to Computer Science itself, Stanford offers several interdisciplinary degrees with a substantial computer science component. The Symbolic Systems major (in the School of Humanities and Sciences) offers an opportunity to explore computer science and its relation to linguistics, philosophy, and psychology. The Mathematical and Computational Sciences major (also Humanities and Sciences) allows students to explore computer science along with more mathematics, statistics, and operations research.

Computer Science (CS)

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

Mission of the Undergraduate Program in Computer Science

The mission of the undergraduate program in Computer Science is to develop students' breadth of knowledge across the subject areas of computer science, including their ability to apply the defining processes of computer science theory, abstraction, design, and implementation to solve problems in the discipline. Students take a set of core courses. After learning the essential programming techniques and the mathematical foundations of computer science, students take courses in areas such as programming techniques, automata and complexity theory, systems programming, computer architecture, analysis of algorithms, artificial intelligence, and applications. The program prepares students for careers in government, law, the corporate sector, and for graduate study.

Requirements

Mathematics (26 units minimum)--

CS 103 CS 109

MATH 19 MATH 20 MATH 21 Plus two electives 2

Mathematical Foundations of Computing

Introduction to Probability for Computer Scientists Calculus 1 Calculus 1 Calculus 1

Units 5 5

3 3 4

Science (11 units minimum)--

Units

PHYSICS 41

Mechanics

4

or PHYSICS 21 Mechanics, Fluids, and Heat

or PHYSICS 41E Mechanics, Concepts, Calculations, and Context

PHYSICS 43

Electricity and Magnetism

4

or PHYSICS 23 Electricity, Magnetism, and Optics

Science elective 3

3

Technology in Society (3-5 units)-- One course; course chosen must be on the SoE Approved Courses list at the year taken; see Basic Requirements 4 in the School of Engineering section

Engineering Fundamentals (13 units minimum; see Basic Requirement 3 in the School of Engineering section)--

Units

CS 106B

Programming Abstractions

5

or CS 106X

Programming Abstractions

ENGR 40M

An Intro to Making: What is EE (or

3-5

ENGR 40A and ENGR 40B)

Fundamentals Elective (May be an ENGR fundamentals or

3-5

an additional CS Depth course. See Fig. 3-4 in the UGHB for

approved ENGR fundamentals list. May not be any CS 106)

*Students who take ENGR 40A or 40M for fewer than 5 units are required to take 1-2 additional units of ENGR Fundamentals (13 units minimum), or 1-2 additional units of Depth.

Writing in the Major--

Select one of the following:

CS 181W

Computers, Ethics, and Public Policy

CS 182W

Ethics, Public Policy, and Technological Change

CS 191W

Writing Intensive Senior Project

CS 194W

Software Project

CS 210B

Software Project Experience with Corporate Partners

CS 294W

Writing Intensive Research Project in Computer Science

Units

Computer Science Core (15 units)--

CS 107 or CS 107E

CS 110 or CS 111

CS 161

Computer Organization and Systems Computer Systems from the Ground Up Principles of Computer Systems Operating Systems Principles Design and Analysis of Algorithms

Units 5

5

5

Senior Project (3 units)--

CS 191 CS 191W CS 194 CS 194H CS 194W CS 210B

CS 294S

CS 294W

Senior Project 7 Writing Intensive Senior Project 7

Software Project

User Interface Design Project

Software Project

Software Project Experience with Corporate Partners

Research Project in Software Systems and Security

Writing Intensive Research Project in Computer Science

Units

Computer Science Depth B.S.

Choose one of the following ten CS degree tracks (a track must consist of at least 25 units and 7 classes):

Artificial Intelligence Track--

CS 221

Artificial Intelligence: Principles and Techniques

Select two courses, each from a different area:

Area I, AI Methods:

CS 228

Probabilistic Graphical Models: Principles and Techniques

CS 229

Machine Learning

CS 234

Reinforcement Learning

CS 238

Decision Making under Uncertainty

Area II, Natural Language Processing:

CS 124

From Languages to Information

Units 4

Stanford Bulletin 2019-20

CS 224N

Natural Language Processing with Deep Learning

CS 224S

Spoken Language Processing

CS 224U

Natural Language Understanding

Area III, Vision:

CS 131

Computer Vision: Foundations and Applications

CS 231A

Computer Vision: From 3D Reconstruction to Recognition

CS 231N

Convolutional Neural Networks for Visual Recognition

Area IV, Robotics:

CS 223A

Introduction to Robotics

CS 237A

Principles of Robot Autonomy I

Select one additional course from the Areas above or from the following:

AI Methods:

CS 157

Computational Logic

CS 205L

Continuous Mathematical Methods with an Emphasis on Machine Learning

CS 230

Deep Learning

CS 236

Deep Generative Models

STATS 315A

Modern Applied Statistics: Learning

STATS 315B

Modern Applied Statistics: Data Mining

Comp Bio:

CS 235

Computational Methods for Biomedical Image Analysis and Interpretation

CS 279

Computational Biology: Structure and Organization of Biomolecules and Cells

CS 371

Computational Biology in Four Dimensions

Information and the Web:

CS 276

Information Retrieval and Web Search

CS 224W

Machine Learning with Graphs

Other:

CS 151

Logic Programming

CS 227B

General Game Playing

CS 379

Interdisciplinary Topics (Offered occasionally)

Robotics and Control:

CS 327A

Advanced Robotic Manipulation

CS 329

Topics in Artificial Intelligence (with advisor approval)

ENGR 205

Introduction to Control Design Techniques

MS&E 251

Introduction to Stochastic Control with Applications

MS&E 351

Dynamic Programming and Stochastic Control

Track Electives: at least three additional courses selected from the Areas and lists above, general CS electives, or the courses listed below. Students can replace one of these electives with a course found at (https:// cs.stanford.edu/explore/): 5

CS 237B

Principles of Robot Autonomy II

CS 257

Logic and Artificial Intelligence

CS 275

Translational Bioinformatics

CS 326

Topics in Advanced Robotic Manipulation

CS 330

Deep Multi-task and Meta Learning

CS 336

CS 338

Physical Human Robot Interaction

Computer Science

3

CS 398 CS 428

EE 263 EE 278

EE 364A EE 364B ECON 286 MS&E 252

MS&E 352

MS&E 355

PHIL 152 PSYCH 204A PSYCH 204B PSYCH 209 STATS 200 STATS 202 STATS 205

Computational Education Computation and Cognition: The Probabilistic Approach Introduction to Linear Dynamical Systems Introduction to Statistical Signal Processing Convex Optimization I Convex Optimization II Game Theory and Economic Applications Decision Analysis I: Foundations of Decision Analysis Decision Analysis II: Professional Decision Analysis Influence Diagrams and Probabilistics Networks Computability and Logic Human Neuroimaging Methods Computational Neuroimaging Neural Network Models of Cognition Introduction to Statistical Inference Data Mining and Analysis Introduction to Nonparametric Statistics

Biocomputation Track--

The Mathematics, Science, and Engineering Fundamentals requirements are non-standard for this track. See Handbook for Undergraduate Engineering Programs for details.

Select one of the following:

CS 221

Artificial Intelligence: Principles and Techniques

CS 228

Probabilistic Graphical Models: Principles and Techniques

CS 229

Machine Learning

CS 231A

Computer Vision: From 3D Reconstruction to Recognition

Select one of the following:

CS 235

Computational Methods for Biomedical Image Analysis and Interpretation

CS 270

Modeling Biomedical Systems

CS 273A

The Human Genome Source Code

CS 274

Representations and Algorithms for Computational Molecular Biology

CS 275

Translational Bioinformatics

CS 279

Computational Biology: Structure and Organization of Biomolecules and Cells

One additional course from the lists above or the following:

CS 124

From Languages to Information

CS 145

Data Management and Data Systems

CS 147

Introduction to Human-Computer Interaction Design

CS 148

Introduction to Computer Graphics and Imaging

CS 248

Interactive Computer Graphics

One course selected from the following:

CS 108

Object-Oriented Systems Design

CS 124

From Languages to Information

CS 131

Computer Vision: Foundations and Applications

Units 3-4

3-4

3-4 4

3-4 3-4

Stanford Bulletin 2019-20

4 Computer Science

CS 140

or CS 140E CS 142 CS 143 CS 144 CS 145 CS 146

CS 147

CS 148

CS 149 CS 151 CS 154 CS 155 CS 157

or PHIL 151 CS 163 CS 166 CS 168 CS 190 CS 195

CS 197 CS 205L

CS 210A

CS 217

CS 221

CS 223A CS 224N

CS 224S CS 224U CS 224W CS 225A CS 227B CS 228

CS 229 CS 229M CS 230 CS 231A

CS 231N

CS 232 CS 233 CS 234 CS 235

CS 236 CS 237A CS 237B

Operating Systems and Systems

3-4

Programming 4

Operating systems design and implementation

Web Applications

3

Compilers

3-4

Introduction to Computer Networking

3-4

Data Management and Data Systems

3-4

Introduction to Game Design and

3

Development

Introduction to Human-Computer

3-5

Interaction Design

Introduction to Computer Graphics and

3-4

Imaging

Parallel Computing

3-4

Logic Programming

3

Introduction to the Theory of Computation

3-4

Computer and Network Security

3

Computational Logic

3

Metalogic

The Practice of Theory Research

3

Data Structures

3-4

The Modern Algorithmic Toolbox

3-4

Software Design Studio

3-4

Supervised Undergraduate Research (4

3-4

units max)

Computer Science Research

4

Continuous Mathematical Methods with an

3

Emphasis on Machine Learning

Software Project Experience with Corporate 3-4 Partners

Hardware Accelerators for Machine

3-4

Learning

Artificial Intelligence: Principles and

3-4

Techniques

Introduction to Robotics

3

Natural Language Processing with Deep

3-4

Learning

Spoken Language Processing

2-4

Natural Language Understanding

3-4

Machine Learning with Graphs

3-4

Experimental Robotics

3

General Game Playing

3

Probabilistic Graphical Models: Principles

3-4

and Techniques

Machine Learning

3-4

Machine Learning Theory

3

Deep Learning

3-4

Computer Vision: From 3D Reconstruction

3-4

to Recognition

Convolutional Neural Networks for Visual

3-4

Recognition

Digital Image Processing

3

Geometric and Topological Data Analysis

3

Reinforcement Learning

3

Computational Methods for Biomedical

3-4

Image Analysis and Interpretation

Deep Generative Models

3

Principles of Robot Autonomy I

3-5

Principles of Robot Autonomy II

3-4

CS 238 CS 240 CS 240LX CS 242 CS 243 CS 244 CS 244B CS 245 CS 246 CS 247 CS 248 CS 251

CS 252 CS 254 CS 254B CS 255 CS 261 CS 263 CS 265

CS 269Q

CS 269I

CS 270 CS 271 CS 272

CS 273A CS 273B

CS 274

CS 275 CS 276 CS 278 CS 279

CS 330 CS 336

CS 348 CS 351 CS 352 CS 369L

CS 371 CS 398 CME 108 EE 180 EE 263 EE 282 EE 364A BIOE 101 MS&E 152 MS&E 252

Stanford Bulletin 2019-20

Decision Making under Uncertainty

3-4

Advanced Topics in Operating Systems

3

Advanced Systems Laboratory, Accelerated

3

Programming Languages

3

Program Analysis and Optimizations

3-4

Advanced Topics in Networking

3-4

Distributed Systems

3

Principles of Data-Intensive Systems

3

Mining Massive Data Sets

3-4

(Any suffix)

3-4

Interactive Computer Graphics

3-4

Cryptocurrencies and blockchain

3

technologies

Analysis of Boolean Functions

3

Computational Complexity

3

Computational Complexity II

3

Introduction to Cryptography

3

Optimization and Algorithmic Paradigms

3

Counting and Sampling

3

Randomized Algorithms and Probabilistic

3

Analysis

Elements of Quantum Computer

3

Programming

Incentives in Computer Science (Not Given

3

This Year)

Modeling Biomedical Systems

3

Artificial Intelligence in Healthcare

3-4

Introduction to Biomedical Informatics

3-5

Research Methodology

The Human Genome Source Code

3

Deep Learning in Genomics and

3

Biomedicine

Representations and Algorithms for

3-4

Computational Molecular Biology

Translational Bioinformatics

4

Information Retrieval and Web Search

3

Social Computing

3

Computational Biology: Structure and

3

Organization of Biomolecules and Cells

Deep Multi-task and Meta Learning

3

(Robot Perception and Decision Making: not offered this year)

(any suffix)

Open Problems in Coding Theory

3

Pseudo-Randomness

3-4

Algorithmic Perspective on Machine

3

Learning

Computational Biology in Four Dimensions

3

Computational Education

4

Introduction to Scientific Computing

3

Digital Systems Architecture

4

Introduction to Linear Dynamical Systems

3

Computer Systems Architecture

3

Convex Optimization I

3

Systems Biology

3

Introduction to Decision Analysis

3-4

Decision Analysis I: Foundations of

3-4

Decision Analysis

Computer Science

5

STATS 206

Applied Multivariate Analysis

STATS 315A

Modern Applied Statistics: Learning

STATS 315B

Modern Applied Statistics: Data Mining

GENE 211

Genomics

One course from the following:

CS 145

Data Management and Data Systems

CS 147

Introduction to Human-Computer Interaction Design

CS 221

Artificial Intelligence: Principles and Techniques

CS 228

Probabilistic Graphical Models: Principles and Techniques

CS 229

Machine Learning

CS 235

Computational Methods for Biomedical Image Analysis and Interpretation

CS 270

Modeling Biomedical Systems

CS 271

Artificial Intelligence in Healthcare

CS 273A

The Human Genome Source Code

CS 273B

Deep Learning in Genomics and Biomedicine

CS 274

Representations and Algorithms for Computational Molecular Biology

CS 275

Translational Bioinformatics

CS 279

Computational Biology: Structure and Organization of Biomolecules and Cells

CS 371

Computational Biology in Four Dimensions

EE 263

Introduction to Linear Dynamical Systems

EE 364A

Convex Optimization I

MS&E 152

Introduction to Decision Analysis

MS&E 252

Decision Analysis I: Foundations of Decision Analysis

STATS 206

Applied Multivariate Analysis

STATS 315A

Modern Applied Statistics: Learning

STATS 315B

Modern Applied Statistics: Data Mining

GENE 211

Genomics

One course selected from the list above or the following:

CHEMENG 150

Biochemical Engineering

CHEMENG 174

Environmental Microbiology I

APPPHYS 294

Cellular Biophysics

BIO 104

Advance Molecular Biology: Epigenetics and Proteostasis

BIO 118

(Not Given This Year)

BIO 214

Advanced Cell Biology

BIO 230

Molecular and Cellular Immunology

CHEM 141

The Chemical Principles of Life I

CHEM 171

Foundations of Physical Chemistry

BIOC 241

Biological Macromolecules

One course from the following:

BIOE 220

Introduction to Imaging and Image-based Human Anatomy

CHEMENG 150

Biochemical Engineering

CHEMENG 174

Environmental Microbiology I

CS 235

Computational Methods for Biomedical Image Analysis and Interpretation

CS 274

Representations and Algorithms for Computational Molecular Biology

CS 279

Computational Biology: Structure and Organization of Biomolecules and Cells

CS 371

Computational Biology in Four Dimensions

3 ME 281

Biomechanics of Movement

3

3 APPPHYS 294

Cellular Biophysics

3

3 BIO 104

3 3-5 BIO 112 3-4 BIO 118 3-5 BIO 158

BIO 183

Advance Molecular Biology: Epigenetics

5

and Proteostasis

Human Physiology

4

(Not Given This Year)

4

Developmental Neurobiology

4

Theoretical Population Genetics

3

3-4 BIO 214

Advanced Cell Biology

4

BIO 230

Molecular and Cellular Immunology

4

3-4 CHEM 171

Foundations of Physical Chemistry

4

CHEM 141

The Chemical Principles of Life I

4

3-4 BIOC 241

Biological Macromolecules

3-5

3-4 DBIO 210

Developmental Biology

4

GENE 211

Genomics

3

3

SURG 101

Regional Study of Human Structure

5

3-4

3 Computer Engineering Track--

3

Units

3-4

For this track there is a 10 unit minimum for ENGR Fundamentals and a 29 unit minimum for Depth (for track and elective courses)

4 EE 108

Digital System Design

4

3 EE 180

Digital Systems Architecture

4

Select two of the following:

8

3

EE 101A

Circuits I

3

EE 101B

Circuits II

3

EE 102A

Signal Processing and Linear Systems I

3-4

EE 102B

Signal Processing and Linear Systems II

3-4 Satisfy the requirements of one of the following concentrations:

1) Digital Systems Concentration

3

CS 140

3

Operating Systems and Systems Programming 4

3

or CS 140E Operating systems design and implementation

3

or CS 143

Compilers

EE 109

Digital Systems Design Lab

3

EE 271

Introduction to VLSI Systems

3

Plus two of the following (6-8 units):

3

CS 140

5

Operating Systems and Systems Programming (if not counted above) 4

or CS 140E Operating systems design and implementation

4

or CS 143

4

CS 144

4

CS 149

4

CS 190

4

CS 217

3-5 CS 244

Compilers Introduction to Computer Networking Parallel Computing Software Design Studio Hardware Accelerators for Machine Learning Advanced Topics in Networking

3

EE 273

EE 282

Digital Systems Engineering Computer Systems Architecture

3

2) Robotics and Mechatronics Concentration

3

CS 205L

3-4 CS 223A

Continuous Mathematical Methods with an Emphasis on Machine Learning

Introduction to Robotics

3-4

ME 210

ENGR 105

Introduction to Mechatronics Feedback Control Design

3

Plus one of the following (3-4 units):

CS 225A

Experimental Robotics

3

Stanford Bulletin 2019-20

6 Computer Science

CS 231A

Computer Vision: From 3D Reconstruction to Recognition

ENGR 205

Introduction to Control Design Techniques

ENGR 207B

Linear Control Systems II

3) Networking Concentration

CS 140

Operating Systems and Systems Programming (CS 140E can substitute for CS 140) 4

CS 144

Introduction to Computer Networking

Plus three of the following (9-11 units):

CS 240

Advanced Topics in Operating Systems

or CS 240LX Advanced Systems Laboratory, Accelerated

CS 241

Embedded Systems Workshop

CS 244

Advanced Topics in Networking

CS 244B

Distributed Systems

EE 179

Analog and Digital Communication Systems

Graphics Track--

CS 148

Introduction to Computer Graphics and Imaging

CS 244

Advanced Topics in Networking

Select one of the following: 6

CS 205L

Continuous Mathematical Methods with an Emphasis on Machine Learning

Units 4

4 3-5

CME 104 CME 108

Linear Algebra and Partial Differential Equations for Engineers (Note: students taking CME 104 are also required to take its prerequisite course, CME 102)

Introduction to Scientific Computing

MATH 52 MATH 113

Integral Calculus of Several Variables Linear Algebra and Matrix Theory

Select two of the following:

6-8

CS 146

Introduction to Game Design and Development

CS 231A

Computer Vision: From 3D Reconstruction to Recognition

or CS 131 CS 233

Computer Vision: Foundations and Applications Geometric and Topological Data Analysis

CS 348 CS 448

(Computer Graphics: any suffix) Topics in Computer Graphics

Track Electives: at least two additional courses from the lists

6-8

above, the general CS electives list, or the courses listed below.

Students can replace one of these electives with a course found

at: ( explore/): 5

ARTSTUDI 160 Intro to Digital / Physical Design

ARTSTUDI 170 ARTSTUDI 179

Photography I: Black and White Digital Art I

CME 302 CME 306

Numerical Linear Algebra

Numerical Solution of Partial Differential Equations

EE 168 EE 262

Introduction to Digital Image Processing Three-Dimensional Imaging

EE 264 EE 278

EE 368

Digital Signal Processing Introduction to Statistical Signal Processing Digital Image Processing

ME 101 PSYCH 30 PSYCH 221

Visual Thinking Introduction to Perception Image Systems Engineering

Human-Computer Interaction Track--

CS 147 CS 247

Introduction to Human-Computer Interaction Design

(Any suffix)

Units 5

4

CS 347 CS 142

Human-Computer Interaction: Foundations

4

and Frontiers

Web Applications

3

Any one of the following:

CS 194H

User Interface Design Project

CS 206 CS 210A

CS 247

Exploring Computational Journalism Software Project Experience with Corporate Partners (Any suffix beyond the course used above)

CS 278

Social Computing

Any CS 377 'Topics in HCI' of three or more units

CS 448B ME 216M

Data Visualization

Introduction to the Design of Smart Products

At least two additional courses from the above areas or the general CS electives list. Students can replace one of these electives with a course found at () Optional Elective 5

Information Track--

Units

CS 124

From Languages to Information

4

CS 145

Data Management and Data Systems

4

Two courses, from different areas:

6-9

1) Information-based AI applications

CS 224N

Natural Language Processing with Deep Learning

CS 224S

Spoken Language Processing

CS 229

Machine Learning

CS 233

Geometric and Topological Data Analysis

CS 234

Reinforcement Learning

2) Database and Information Systems

CS 140

Operating Systems and Systems Programming 4

or CS 140E Operating systems design and implementation

CS 142

Web Applications

CS 151

Logic Programming

CS 245

Principles of Data-Intensive Systems

CS 246

Mining Massive Data Sets

CS 341

Project in Mining Massive Data Sets

3) Information Systems in Biology

CS 235

Computational Methods for Biomedical Image Analysis and Interpretation

CS 270

Modeling Biomedical Systems

CS 274

Representations and Algorithms for Computational Molecular Biology

4) Information Systems on the Web

CS 224W

Machine Learning with Graphs

CS 276

Information Retrieval and Web Search

Stanford Bulletin 2019-20

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