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Engineering & Computer Science

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Engineering and Computer Science
Date: 
Monday, January 19, 2015 to Sunday, April 19, 2015
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About the Course

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

FAQ: 
  • What is the format of the class?

    The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.

  • How much programming background is needed for the course?

    The course includes programming assignments and some programming background will be helpful.

  • Do I need to buy a textbook for the course?

    No, it is self-contained.

  • Will I get a statement of accomplishment after completing this class?

    Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

Instructor(s): 
Andrew Ng

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Date: 
Monday, January 12, 2015 to Friday, February 20, 2015
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THIS COURSE IS OFFERED THROUGH STANFORD CONTINUING STUDIES.

ABOUT THIS COURSE

Many of the world’s most famous, monumental, and staggering engineering projects or inventions are in fact ancient, some even prehistoric. Stonehenge and related megaliths date back thousands of years. Found off Greece in a shipwreck, the enigmatic Antikythera bronze is an astonishing invention dating before the Roman empire. Even if Hero of Alexandria’s 1st century ce steam engines were only theoretical, they deserve mention alongside his pneumatics and mechanics innovations. Some of the most remarkable achievements in antiquity include Roman roads and bridges, and the advent of concrete and hydrological technology such as aqueducts. Add to this the qanat canals and irrigation system of Persia, the pyramids of Egypt, the stone cities and road networks of the Incas and their ancestors in South America, and the urban and sculptural stoneworking of the Aztecs in Central America. All of these feats of engineering, occurring long before the Industrial Revolution, will be the subject of this course.

Thanks to the flexibility of the online format, this course can be taken anywhere, anytime—a plus for students who lead busy lives or for whom regular travel to the Stanford campus is not possible. While necessarily structured differently from an on-campus classroom course, this course maintains a similar level of instructor engagement through videos, interactive exercises, and discussion with fellow students, as well as optional online video conferencing sessions.

Tuition Applies.

INSTRUCTOR

Patrick Hunt, Former Director, Stanford Alpine Archaeology Project; Research Associate in Archeoethnobotany, Institute for EthnoMedicine

Patrick Hunt has taught at Stanford since 1993 and is also an associate at the UCLA Center for Medieval and Renaissance Studies. He is the author of fourteen books, including Ten Discoveries That Rewrote History, Myth and Art in Ekphrasis, and Critical Insights: The Inferno. Hunt was the director of the National Geographic Society Hannibal Expedition. He received a PhD from the Institute of Archaeology, UCL. - See more at: http://continuingstudies.stanford.edu/courses/detail/20142_ARC-122-W#sth...

Archaeology and Ancient Engineering

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Date: 
Monday, January 19, 2015 to Saturday, March 14, 2015
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About the Course

In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

Course Syllabus

Week 1: Introduction.  Asymptotic analysis including big-oh notation.  Divide-and-conquer algorithms for sorting, counting inversions, matrix multiplication, and closest pair.

Week 2: Running time analysis of divide-and-conquer algorithms.  The master method.  Introduction to randomized algorithms, with a probability review.  QuickSort.  

Week 3: More on randomized algorithms and probability.  Computing the median in linear time.  A randomized algorithm for the minimum graph cut problem.

Week 4: Graph primitives.  Depth- and breadth-first search.  Connected components in undirected graphs.  Topological sort in directed acyclic graphs.  Strongly connected components in directed graphs.

Week 5: Dijkstra's shortest-path algorithm.  Introduction to data structures.  Heaps and applications.

Week 6: Further data structures.  Hash tables and applications.  Balanced binary search trees.

Recommended Background

How to program in at least one programming language (like C, Java, or Python); and familiarity with proofs, including proofs by induction and by contradiction.  At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors.  

Suggested Readings

No specific textbook is required for the course.  Much of the course material is covered by the well-known textbooks on algorithms, and the student is encouraged to consult their favorite for additional information.

Course Format

The class will consist of lecture videos, generally between 10 and 15 minutes in length. These usually integrated quiz questions. There will also be standalone homeworks and programming assignments that are not part of video lectures, and a final exam.

FAQ

  • Will I get a statement of accomplishment after completing this class?

    Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

  • What is the format of the class?

    The class consists of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures. There will be approximately two hours worth of video content per week.

  • What should I know to take this class? How to program in at least one programming language (like C, Java, or Python); familiarity with proofs, including proofs by induction and by contradiction; and some discrete probability, like how to compute the probability that a poker hand is a full house. At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors.
Instructor(s): 
Tim Roughgarden
Algorithms: Design and Analysis, Part I  Course Image

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Date: 
Tuesday, January 13, 2015 to Wednesday, March 18, 2015
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ABOUT THIS COURSE

This course covers key topics in the use of quantum mechanics in many modern applications in science and technology, introduces core advanced concepts such as spin, identical particles, the quantum mechanics of light, the basics of quantum information, and the interpretation of quantum mechanics, and covers the major ways in which quantum mechanics is written and used in modern practice. It follows on directly from the QMSE-01 "Quantum Mechanics for Scientists and Engineers" course, and is also accessible to others who have studied some quantum mechanics at the equivalent of a first junior or senior college-level physics quantum mechanics course. All of the material for the QMSE-01 course is also provided as a resource. The course should prepare the student well to understand quantum mechanics as it is used in a wide range of current applications and areas and provide a solid grounding for deeper studies of specific more advanced areas.

COURSE SYLLABUS

Quantum mechanics in crystals

Crystal structures, the Bloch theorem that simplifies quantum mechanics in crystals, and other useful concepts for understanding semiconductor devices, such as density of states, effective mass, quantum confinement in nanostructures, and important example problems like optical absorption in semiconductors, a key process behind all optoelectronics.

Methods for one-dimensional problems

How to understand and calculate tunneling current. The transfer matrix technique, a very simple and effective technique for calculating quantum mechanical waves and states.

Spin and identical particles

The purely quantum mechanical idea of spin, and how to represent and visualize it. The general ideas of identical particles in quantum mechanics, including fermions and bosons, their properties and the states of multiple identical particles.

Quantum mechanics of light

Representing light quantum mechanically, including the concept of photons, and introducing the ideas of annihilation and creation operators.

Interaction of different kinds of particles

Describing interactions and processes using annihilation and creation operators for fermions and bosons, including the important examples of stimulated and spontaneous emission that correctly explain all light emitters, from lasers to light bulbs.

Mixed states and the density matrix

Introducing the idea of mixed states to describe how quantum mechanical systems interact with the rest of the complex world around us, and the notation and use of the density matrix to describe and manipulate these.

Quantum measurement and quantum information

Introducing the no-cloning theorem, quantum cryptography, quantum entanglement and the basic ideas of quantum computing and teleportation, and returning to the idea of measurement in quantum mechanics, including the surprising results of Bell’s inequalities.

Interpretation of quantum mechanics

A brief introduction to some of the different approaches to the difficult problem of understanding what quantum mechanics really means!

PREREQUISITES

The course is designed to build on a first course on quantum mechanics at the junior or senior college level, so students should have at least that background. The material here is specifically matched to follow on from the Stanford Online QMSE-01 "Quantum Mechanics for Scientists and Engineers" class, and all the material from that class is provided as background in the online course materials here. No additional background beyond that class is presumed here.

FAQ: 

Do I need to buy a textbook?

You do not need to buy a textbook; the course is self-contained. My book “Quantum Mechanics for Scientists and Engineers” (Cambridge, 2008) is an optional additional resource for the course. It follows essentially the same syllabus, has additional problems and exercises, allows you to go into greater depth on some ideas, and also contains many additional topics for further study.

How much of a time commitment will this course be?

You should expect this course to require 7 – 10 hours of work per week.

Does this course carry any kind of Stanford University credit?

No.

Will I get a Statement of Accomplishment?

Yes, students who score at least 70% will pass the course and receive a Statement of Accomplishment. Students who score at least 90% will receive a Statement of Accomplishment with distinction.

We recommend taking this course on a standard computer using Google Chrome as your internet browser. We are not yet optimized for mobile devices.

COURSE STAFF

David Miller

David Miller is the W. M. Keck Foundation Professor of Electrical Engineering and, by Courtesy, Professor of Applied Physics, both at Stanford University. He received his B. Sc. and Ph. D. degrees in Physics in Scotland, UK from St. Andrews University and Heriot-Watt University, respectively. Before moving to Stanford in 1996, he worked at AT&T Bell Laboratories for 15 years. His research interests have included physics and applications of quantum nanostructures, including invention of optical modulator devices now widely used in optical fiber communications, and fundamentals and applications of optics and nanophotonics. He has received several awards and honorary degrees for his work, holds over 70 US Patents, is a Fellow of many major professional societies in science and engineering, including IEEE, APS, OSA, the Royal Society of London, and the Royal Society of Edinburgh, and is a member of both the National Academy of Sciences and the National Academy of Engineering in the US. He has taught quantum mechanics at Stanford for more than 10 years to a broad range of students ranging from physics and engineering undergraduates to graduate engineers and scientists in many disciplines.

Quantum Mechanics for Scientists and Engineers 2  Course Image

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Date: 
Monday, January 6, 2014 to Thursday, March 19, 2015
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ABOUT THE COURSE

Chemical transformations research provides an opportune platform for discovering the entrepreneurial opportunities and challenges of energy supply and consumption. Through exposure to unbiased inquiry of energy production alternatives you will have the tools needed to move forward and examine the composite of future large energy production systems. This course contributes to the online Molecular Engineering of Energy Technologies Graduate Certificate. Enrollment is open for winter quarter. (Fee Applies)

Topics Include

  • Solar thermal systems
  • Biofuels
  • Photovoltaics
  • Electrochemical devices (batteries and fuel cells)
  • Energy production technologies

Instructors

Energy: Chemical Transformations for Production, Storage and Use

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Date: 
Monday, January 5, 2015 to Friday, March 20, 2015
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About the Course

Combine essential chemical engineering concepts and biological principles to address the needs of the biotech industry. Investigate recombinant DNA technology, synthetic biology and metabolic engineering.  Develop processes and effective solutions to navigate regulatory policies and the manufacturing of products. This course contributes to the online Biotechnology Graduate Certificate. Enrollment is open for winter quarter. (Fee Applies)

Topics Include

  • Elemental stoichiometry of metabolism
  • Fermentation development and control
  • Product isolation and purification
  • Protein folding and formulation

Instructors:

Biochemical Engineering  Course Image

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Date: 
Friday, January 8, 2016 to Saturday, February 20, 2016
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About the Course

This advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered: social choice theory (i.e., collective decision making), mechanism design, and auctions.

Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents.  Over four weeks of lectures, this advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered:  social choice theory (i.e., collective decision making), mechanism design, and auctions.

In the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems.  We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms.  The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory. 

Course Syllabus

There will be four weeks of materials consisting of online videos and problem sets. We recommend that you complete the problem set for each week within that week, although the hard deadline is two weeks from the release date. On the fifth week, we will have a final exam.

Week 1. Social Choice

Week 2. Mechanism Design

Week 3. Efficient Mechanisms

Week 4. Auctions

Week 5-6. Final exam and final problem set.

Recommended Background

You must be comfortable with mathematical thinking and rigorous arguments. Relatively little specific math is required; the course involves lightweight probability theory (for example, you should know what a conditional probability is) and very lightweight calculus (for instance, taking a derivative).

Suggested Readings

The following background readings provide more detailed coverage of the course material:

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, by Yoav Shoham and Kevin Leyton-Brown; Cambridge University Press, 2009. This book has the same structure as the course, and covers most of the same material. It is available as a free PDF download from the link above or for sale as a physical book from (e.g.) amazon.com.

A Brief Introduction to the Basics of Game Theory, by Matthew O. Jackson. These notes offer a quick introduction to the basics of game theory; they are available as a free PDF download.

Course Format

· Videos.  The lectures are delivered via videos, which are broken into small chunks, usually between five and fifteen minutes each. There will be approximately one and a half hours of video content per week. You may watch the lecture videos at your convenience. Lower-resolution videos are also available for those with slow Internet connections.

· Slides.  We have made available pdf files of all the lecture slides.

· Quizzes.  There will be non-graded short "quiz" questions that will follow some of the videos to help you gauge your understanding.

· Online Lab Exercises.  After some of the videos, we will ask you to go online to play some games. These are entirely optional, and are designed to illustrate some of the concepts from the course.

· Problem Sets.  There will also be graded weekly problem sets that you will also answer online, but may work through offline; those must be completed within two weeks of the time that they are posted in order to be graded for full credit. If you miss a problem set deadline, you may complete it before the end of the course for half credit. You may discuss problems from the problem sets with other students in an online forum, without providing explicit answers.

· Final Exam.  There will be an online final exam that you will have to complete within two weeks of its posting. Once you begin the exam, you will have four hours to complete it.

· Screen-side Chats.  We will hold occasional online chats where we answer  questions and discuss topics relevant to the course.

FAQ: 
  • Will I get a statement of accomplishment after completing this class?

    Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructors.

Instructor(s): 
Kevin Leyton-Brown
Game Theory II : Advanced Applications

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Date: 
Monday, January 5, 2015 to Monday, March 9, 2015
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About the Course

Cryptography is an indispensable tool for protecting information in computer systems. This course explains the inner workings of cryptographic primitives and how to correctly use them. Students will learn how to reason about the security of cryptographic constructions and how to apply this knowledge to real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two or more parties generate a shared secret key. We will cover the relevant number theory and discuss public-key encryption and basic key-exchange. Throughout the course students will be exposed to many exciting open problems in the field.

The course will include written homeworks and programming labs. The course is self-contained, however it will be helpful to have a basic understanding of discrete probability theory.

FAQ: 
  • Will I get a statement of accomplishment after completing this class?

    Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

  • What is the format of the class?

    The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments. There will be approximately two hours worth of video content per week.

  • How much programming background is needed for the course?

    The course includes programming assignments and some programming background will be helpful. However, we will hand out lots of starter code that will help students complete the assignments. We will also point to online resources that can help students find the necessary background.

  • What math background is needed for the course?

    The course is mostly self contained, however some knowledge of discrete probability will be helpful. The wikibooks article on discrete probability should give sufficient background.

  • Can I see a preview of the lectures and homework?

    Yes, check out this preview site.

Instructor(s): 
Dan Boneh

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Date: 
Monday, November 3, 2014
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About This Course

This self-paced course will discuss the major ideas used today in the implementation of programming language compilers, including lexical analysis, parsing, syntax-directed translation, abstract syntax trees, types and type checking, intermediate languages, dataflow analysis, program optimization, code generation, and runtime systems. As a result, you will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines. Along the way we will also touch on how programming languages are designed, programming language semantics, and why there are so many different kinds of programming languages.

The course lectures will be presented in short videos. To help you master the material, there will be in-lecture questions to answer, quizzes, and two exams: a midterm and a final. There will also be homework in the form of exercises that ask you to show a sequence of logical steps needed to derive a specific result, such as the sequence of steps a type checker would perform to type check a piece of code, or the sequence of steps a parser would perform to parse an input string. This checking technology is the result of ongoing research at Stanford into developing innovative tools for education, and we're excited to be the first course ever to make it available to students.

An optional course project is to write a complete compiler for COOL, the Classroom Object Oriented Language. COOL has the essential features of a realistic programming language, but is small and simple enough that it can be implemented in a few thousand lines of code. Students who choose to do the project can implement it in either C++ or Java.

Why Study Compilers?

Everything that computers do is the result of some program, and all of the millions of programs in the world are written in one of the many thousands of programming languages that have been developed over the last 60 years. Designing and implementing a programming language turns out to be difficult; some of the best minds in computer science have thought about the problems involved and contributed beautiful and deep results. Learning something about compilers will show you the interplay of theory and practice in computer science, especially how powerful general ideas combined with engineering insight can lead to practical solutions to very hard problems. Knowing how a compiler works will also make you a better programmer and increase your ability to learn new programming languages quickly.

Prerequisites

No prerequisites are needed for this course.

Frequently Asked Questions

Do I need to buy a textbook?

No, no textbook is required for the class. However, you may find a textbook useful as a reference or to learn more details of some of the ideas discussed in the course. There are a number of good textbooks on compilers; here are three in particular:

  1. Compilers: Principles, Techniques, and Tools (Second Edition)
    Alfred Aho, Monica Lam, Ravi Sethi, and Jeffrey Ullman.
    Addison-Wesley

  2. Modern Compiler Implementation in Java (Second Edition)
    Andrew Appel and Jens Palsberg.
    Cambridge University Press
  3. Engineering a Compiler
    Keith Cooper and Linda Torczon.
    Morgan Kaufman

Will I get a Statement of Accomplishment after completing this class?

Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

How is the class structured?

Each week there will be a number of videos to watch, most of which will include an in-video quiz question to answer. Most weeks there will also be homeworks (done on-line) and a quiz. There will be also be a midterm and a final exam.

How do I ask questions?

There will be an online discussion forum in which students can ask questions and receive answers. As this course is self-paced, the forum will be largely unmoderated. Answers will come from fellow participants, as a result.

How much does the course cost?

The course is free and there is no charge for registering on the site.

How much time will I need to apply to this course each week?

The estimated effort per week will vary per student, but basically, you should expect to spend:

  • * 3 hours per week in this course if you are not planning to do the programming assignments.
  • * 6-10 hours per week for experienced programmers.

Your mileage will vary, depending on how well you already know the programming language, how long it takes to learn to use the tools and libraries, and how experienced you are at debugging.       

Instructor(s): 
Alex Aiken
Compilers

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Date: 
Tuesday, September 30, 2014
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About This Course

This course aims to teach quantum mechanics to anyone with a reasonable college-level understanding of physical science or engineering. Quantum mechanics was once mostly of interest to physicists, chemists and other basic scientists. Now the concepts and techniques of quantum mechanics are essential in many areas of engineering and science such as materials science, nanotechnology, electronic devices, and photonics. This course is a substantial introduction to quantum mechanics and how to use it. It is specifically designed to be accessible not only to physicists but also to students and technical professionals over a wide range of science and engineering backgrounds.

Course Topics

Introduction to quantum mechanics

How quantum mechanics is important in the everyday world, the bizarre aspects and continuing evolution of quantum mechanics, and how we need it for engineering much of modern technology.

Schroedinger’s wave equation

Getting to Schroedinger’s wave equation. Key ideas in using quantum mechanical waves — probability densities, linearity. The "two slit" experiment and its paradoxes.

Getting "quantum" behavior

The "particle in a box", eigenvalues and eigenfunctions. Mathematics of quantum mechanical waves.

Quantum mechanics of systems that change in time

Time variation by superposition of wave functions. The harmonic oscillator. Movement in quantum mechanics — wave packets, group velocity and particle current.

Measurement in quantum mechanics

Operators in quantum mechanics — the quantum-mechanical Hamiltonian. Measurement and its paradoxes — the Stern-Gerlach experiment.

Writing down quantum mechanics simply

A simple general way of looking at the mathematics of quantum mechanics — functions, operators, matrices and Dirac notation. Operators and measurable quantities. The uncertainty principle.

The hydrogen atom

Angular momentum in quantum mechanics — atomic orbitals. Quantum mechanics with more than one particle. Solving for the the hydrogen atom. Nature of the states of atoms.

How to solve real problems

Approximation methods in quantum mechanics.

Prerequisites

The course is approximately at the level of a first quantum mechanics class in physics at a third-year college level or above, but it is specifically designed to be suitable and useful also for those from other science and engineering disciplines.

The course emphasizes conceptual understanding rather than a heavily mathematical approach, but some amount of mathematics is essential for understanding and using quantum mechanics. The course presumes a mathematics background that includes basic algebra and trigonometry, functions, vectors, matrices, complex numbers, ordinary differential and integral calculus, and ordinary and partial differential equations.

In physics, students should understand elementary classical mechanics (Newton’s Laws) and basic ideas in electricity and magnetism at a level typical of first-year college physics. (The course explicitly does not require knowledge of more advanced concepts in classical mechanics, such as Hamiltonian or Lagrangian approaches, or in electromagnetism, such as Maxwell’s equations.) Some introductory exposure to modern physics, such as the ideas of electrons, photons, and atoms, is helpful but not required.

The course will include “refresher” resources for the required mathematics and physics background.

Course Staff

David Miller

David Miller is the W. M. Keck Foundation Professor of Electrical Engineering and, by Courtesy, Professor of Applied Physics, both at Stanford University. He received his B. Sc. and Ph. D. degrees in Physics in Scotland, UK from St. Andrews University and Heriot-Watt University, respectively. Before moving to Stanford in 1996, he worked at AT&T Bell Laboratores for 15 years. His research interests have included physics and applications of quantum nanostructures, including invention of optical modulator devices now widely used in optical fiber communications, and fundamentals and applications of optics and nanophotonics. He has received several awards and honorary degrees for his work, is a Fellow of many major professional societies in science and engineering, including the Royal Society of London, and is a member of both the National Academy of Sciences and the National Academy of Engineering in the US. He has taught quantum mechanics at Stanford for more than 10 years to a broad range of students ranging from physics and engineering undergraduates to graduate engineers and scientists in many disciplines.

Frequently Asked Questions

Do I need to buy a textbook?

You do not need to buy a textbook; the course is self-contained. My book “Quantum Mechanics for Scientists and Engineers” (Cambridge, 2008) is an optional additional resource for the course. It follows essentially the same syllabus, has additional problems and exercises, allows you to go into greater depth on some ideas, and also contains many additional topics for further study.

How much of a time commitment will this course be?

You should expect this course to require 7 – 10 hours of work per week.

Does this course carry any kind of Stanford University credit?

No.

Will I get a Statement of Accomplishment?

Yes, students who score at least 70% will pass the course and receive a Statement of Accomplishment. Students who score at least 90% will receive a Statement of Accomplishment with distinction.

We recommend taking this course on a standard computer using Google Chrome as your internet browser. We are not yet optimized for mobile devices.

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