Stanford Computer Science Courses

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Stanford Masters Computer Science Degree

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.

Studying at Stanford computer Science courses, provides one with the basic foundation and knowledge of the course. The Foundations in Computer Science Graduate Certificate, MIT Computer Science, Stanford Computer Science masters provides a solid course of study in the mathematical foundations of computing as well as important aspects of computer programming.

Top 6 Most Popular Subjects In Computer Science

Stanford to offer free online CS class during pandemic | The Stanford Daily

Is computer science a difficult course? Well, most people think so. Being a technical course, it is obvious that most people expect to encounter some difficulties when studying it. However, most people believe that generally, computer science is not that hard. There are some few topics that are very simple to grasp. However, there are other topics that seem to be quite hard.
Instead of generalizing the whole course, let’s look at some of the hardest topics or subjects in Computer science.

  1. Artificial Intelligence
    Artificial Intelligence (AI) tops the list of the most difficult subjects in Computer Science. It focuses on teaching students how to program intelligent machines. These are simply machines that are programmed to think and act like real human beings. The intelligent machine should have particular traits that are needed for solving problems. These traits include the ability to learn, reason, perceive and accepting changes depending on various circumstances.
    So, why is AI a difficult topic in computer science? The first reason is it requires a cross-disciplinary approach. You need to combine different disciplines of computer science in order to understand and implement the theories of AI. Some of these disciplines include programming, mathematics, psychology, linguistics and even database management. Combining all these disciplines into one product is not a walk in the park.
    Another reason why this subject is difficult is the evolving nature of AI technology. AI is not a static field. It keeps changing as the technology advances with time. The concepts that worked a few years ago may not be applied now. This means that AI students are always subjected to new concepts every time.
    Otherwise, AI is one of the most lucrative fields in computer science. There is no doubt that AI experts are in a very high demand.
  2. Theory Of Computation
    As a computer science student, you don’t just need to use your computer to solve problems. You need to have an in-depth understanding of how the computer is able to come to a particular solution. The theory of computation is a topic in computer science that elaborates how problems can be solved using a particular algorithm and model of computation.
    Basically, the theory of computation is divided into three distinct branches. These are computability theory, automata theory, and complexity theory. All these branches will equip you with the knowledge of how to explore the limitations and capabilities of a computer.
    Theory of computation covers the mathematical abstraction of computers which is also known as the model of computation. Here students cover several models including the most common one which is known as the Turing machine.
    Apart from just analyzing how a problem can be solved, the theory of computation also teaches a student to analyze whether the methods and algorithms used will solve the existing problems effectively. This means that computer scientists have to look at several other aspects including the memory space required and the time that will be taken to come up with the solution.
  1. Microprocessors
    Another computer science topic that is deemed difficult is the microprocessor. Microprocessors are also known as logic chips and are the engines of the computers. A typical microprocessor contains all the central processing unit functions. It performs both the arithmetic and logic functions of a computer.
    Sounds easier, right? As a computer science student, you will go beyond defining what a microprocessor is. You will learn how it works and even how to design one. Since microprocessors form an integral part of any computing system, a computer science student must be open to receive lots of information about these devices.
    The topic of the microprocessor is quite wide and very technical. First, you will need to learn about the logical operations and mathematical computations. As if this is not enough you will immerse yourself into some fundamentals of electronics. This is because microprocessors consist of thousands of electronic components such as transistors and integrated circuits. You will also learn about different designs of microprocessors and how each design solves a particular problem.
    This topic will equip you with the relevant knowledge and skills that you will use to be a microprocessor designer.
  2. Advanced Database Systems
    Perhaps you are aware of the basics of a database. Obviously, you didn’t have a hard time understanding the fundamentals of a database. However, advanced database systems is a bit difficult computer science topic. Although it may also cover the fundamentals of a database system, it goes deeper to cover advanced and sophisticated database concepts.
    While the fundamentals of database systems are applied in conventional business applications, advanced database systems go beyond the ordinary business use. They are used to manage data in complex applications, especially in emerging technologies. Despite covering the most sophisticated concepts, the topic also covers the basics of database systems.
  3. Compiler Design
    Compiler design is also ranked among the hardest topics in computer science. First things first, a compiler is a program that converts a program written in a high language into machine language. This topic provides an in-depth information about the whole translation and optimization process.
    Computer science students learn the mechanisms of translation and error detection during the compilation process. They also learn the lexical and syntax analysis during the code generation process. The topic is deemed difficult as it requires one to be good at coding. You need to have a good grasp of various programming languages.
  4. Image Processing And Computer Vision
    Image processing and computer vision are two topics that are closely related. Image processing entails giving a computer the power to add some extra transformations into an image. The computer will make the image more attractive or appealing. On the other hand, computer vision analyzes images and various real-world data in order to produce a more-appealing symbolic information.
    These two topics are quite difficult. They require a student to be fully committed and dedicated. However, we must agree that they have a wide range of applications, especially in the modern world. It is also an evolving topic as learners have to keep learning about emerging technologies.

In conclusion, the above are the most perceived difficult topics in computer science. However, with a positive attitude and determination, you will be able to conquer them.

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 University IT (UIT) and the School of Engineering (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” section of this bulletin.

Computer Science Course Catalog Numbering System

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

DigitDescription
001-099Service courses for nontechnical majors
100-199Other service courses, basic undergraduate
200-299Advanced undergraduate/beginning graduate
300-399Advanced graduate
400-499Experimental
500-599Graduate seminars

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

DigitDescription
00-09Introductory, miscellaneous
10-19Hardware and Software Systems
20-39Artificial Intelligence
40-49Software Systems
50-59Mathematical Foundations of Computing
60-69Analysis of Algorithms
70-79Computational Biology and Interdisciplinary Topics
90-99Independent Study and Practicum

MIT Subjects

Some courses in Electrical Engineering and Computer Science (Course 6)

Basic Undergraduate Subjects

6.0001 Introduction to Computer Science Programming in Python
Prereq: None
U (Fall, Spring; first half of term)
3-0-3 units

Introduction to computer science and programming for students with little or no programming experience. Students develop skills to program and use computational techniques to solve problems. Topics include the notion of computation, Python, simple algorithms and data structures, testing and debugging, and algorithmic complexity. Combination of 6.0001 and 6.0002 counts as REST subject. Final given in the seventh week of the term.

A. Bell, J. V. Guttag

6.0002 Introduction to Computational Thinking and Data Science
Prereq: 6.0001 or permission of instructor
U (Fall, Spring; second half of term)
3-0-3 units

Provides an introduction to using computation to understand real-world phenomena. Topics include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering. Combination of 6.0001 and 6.0002 counts as REST subject.

A. Bell, J. V. Guttag

6.002 Circuits and Electronics
Prereq: Physics II (GIR); Coreq: 2.087 or 18.03
U (Fall, Spring)
3-2-7 units. REST

Fundamentals of linear systems and abstraction modeling through lumped electronic circuits. Linear networks involving independent and dependent sources, resistors, capacitors and inductors. Extensions to include nonlinear resistors, switches, transistors, operational amplifiers and transducers. Dynamics of first- and second-order networks; design in the time and frequency domains; signal and energy processing applications. Design exercises. Weekly laboratory with microcontroller and transducers.

J. H. Lang, T. Palacios, D. J. Perreault, J. Voldman

6.003 Signal Processing
Prereq: Calculus I (GIR) and 6.0001
U (Fall, Spring)
6-0-6 units. REST

Fundamentals of signal processing, focusing on the use of Fourier methods to analyze and process signals such as sounds and images. Topics include Fourier series, Fourier transforms, the Discrete Fourier Transform, sampling, convolution, deconvolution, filtering, noise reduction, and compression. Applications draw broadly from areas of contemporary interest with emphasis on both analysis and design.

D. M. Freeman, A. Hartz

6.004 Computation Structures
Prereq: Physics II (GIR) and 6.0001
U (Fall, Spring)
4-0-8 units. REST

Provides an introduction to the design of digital systems and computer architecture. Emphasizes expressing all hardware designs in a high-level hardware language and synthesizing the designs. Topics include combinational and sequential circuits, instruction set abstraction for programmable hardware, single-cycle and pipelined processor implementations, multi-level memory hierarchies, virtual memory, exceptions and I/O, and parallel systems.

Arvind, S. Z. Hanono Wachman, D. Sanchez

6.006 Introduction to Algorithms
Prereq: 6.042[J] and (6.0001 or Coreq: 6.009)
U (Fall, Spring)
4-0-8 units

Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems. Enrollment may be limited.

E. Demaine, S. Devadas

6.008 Introduction to Inference
Prereq: Calculus II (GIR) or permission of instructor
U (Fall)
4-4-4 units. Institute LAB

Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects. Distributions, marginalization, conditioning, and structure, including graphical and neural network representations. Belief propagation, decision-making, classification, estimation, and prediction. Sampling methods and analysis. Introduces asymptotic analysis and information measures. Computational laboratory component explores the concepts introduced in class in the context of contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

P. Golland, G. W. Wornell

6.009 Fundamentals of Programming
Prereq: 6.0001
U (Fall, Spring)
2-4-6 units. Institute LAB

Introduces fundamental concepts of programming. Designed to develop skills in applying basic methods from programming languages to abstract problems. Topics include programming and Python basics, computational concepts, software engineering, algorithmic techniques, data types, and recursion. Lab component consists of software design, construction, and implementation of design. Enrollment may be limited.

D. S. Boning, A. Chlipala, S. Devadas, A. Hartz

6.01 Introduction to EECS via Robotics
Prereq: 6.0001 or permission of instructor
Acad Year 2020-2021: Not offered
Acad Year 2021-2022: U (Spring)
2-4-6 units. Institute LAB

An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Key issues in the design of engineered artifacts operating in the natural world: measuring and modeling system behaviors; assessing errors in sensors and effectors; specifying tasks; designing solutions based on analytical and computational models; planning, executing, and evaluating experimental tests of performance; refining models and designs. Issues addressed in the context of computer programs, control systems, probabilistic inference problems, circuits and transducers, which all play important roles in achieving robust operation of a large variety of engineered systems.

D. M. Freeman, A. Hartz, L. P. Kaelbling, T. Lozano-Perez

6.011 Signals, Systems and Inference
Prereq: 6.003 and (6.008, 6.041, or 18.600)
U (Spring)
4-0-8 units

Covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.

A. V. Oppenheim, G. C. Verghese

6.012 Nanoelectronics and Computing Systems
Prereq: 6.002
U (Fall, Spring)
4-0-8 units

Studies interaction between materials, semiconductor physics, electronic devices, and computing systems. Develops intuition of how transistors operate. Topics range from introductory semiconductor physics to modern state-of-the-art nano-scale devices. Considers how innovations in devices have driven historical progress in computing, and explores ideas for further improvements in devices and computing. Students apply material to understand how building improved computing systems requires knowledge of devices, and how making the correct device requires knowledge of computing systems. Includes a design project for practical application of concepts, and labs for experience building silicon transistors and devices.

A. I. Akinwande, J. Kong, T. Palacios, M. Shulaker

6.013 Electromagnetics Waves and Applications
Prereq: Calculus II (GIR) and Physics II (GIR)
U (Spring)
3-5-4 units

Analysis and design of modern applications that employ electromagnetic phenomena for signals and power transmission in RF, microwaves, optical and wireless communication systems. Fundamentals include dynamic solutions for Maxwell’s equations; electromagnetic power and energy, waves in media, metallic and dielectric waveguides, radiation, and diffraction; resonance; filters; and acoustic analogs. Lab activities range from building to testing of devices and systems (e.g., antenna arrays, radars, dielectric waveguides). Students work in teams on self-proposed maker-style design projects with a focus on fostering creativity, teamwork, and debugging skills. 6.002 and 6.003 are recommended but not required.

K. O’Brien, L. Daniel

6.014 Electromagnetic Fields, Forces and Motion
Subject meets with 6.640
Prereq: Physics II (GIR) and 18.03
U (Fall)
4-0-8 units

Study of electromagnetics and electromagnetic energy conversion leading to an understanding of devices, including electromagnetic sensors, actuators, motors and generators. Quasistatic Maxwell’s equations and the Lorentz force law. Studies of the quasistatic fields and their sources through solutions of Poisson’s and Laplace’s equations. Boundary conditions and multi-region boundary-value problems. Steady-state conduction, polarization, and magnetization. Charge conservation and relaxation, and magnetic induction and diffusion. Extension to moving materials. Electric and magnetic forces and force densities derived from energy, and stress tensors. Extensive use of engineering examples. Students taking graduate version complete additional assignments.

J. L. Kirtley, Jr., J. H. Lang

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