Courses

Calculus, Fourier transforms, probability theory, scientific programming in Python.

Multivariate calculus and linear (or, alternatively, matrix) algebra

Partial differential equations.
Some knowledge of Python, MATLAB or other language is preferred.

Solid undergraduate linear algebra. Confident in following and constructing proofs. Some prior knowledge of the representation theory of finite groups is helpful but not completely necessary. Discuss this carefully with your academic mentor.

Single-variable and multi-variable calculus, linear algebra, ordinary differential equations, real analysis, or equivalent knowledge.

Python and/or R programming, linear algebra, B49 Dynamical systems
Target Students
Students of any discipline who generate time series data or point clouds from multidimensional data. Preferably a quantitative background with intermediate Python, R or C++ skills, but any background in any science is fine as long as they have basic understanding of linear algebra and have basic understanding of linear dynamics and/or taken Mahesh Bandi’s class on nonlinear dynamics which is the standard prerequisite. (Waiving is possible with consent of the instructor)

Students need:
• a keen interest in mathematical abstraction and its practical applications;
• a robust understanding of undergraduate-level single and multivariable calculus, linear algebra, and some exposure to differential equations;
• a curiosity for optimization problems and a willingness to engage in theoretical reasoning and problem-solving;

Programming in Python, e.g B50 Introduction to Scientific Computing. Basic knowledge in neuroscience, e.g. B52 Introduction to Neuroscience or A310 Computational Neuroscience, and statistical machine learning, e.g B46 Introduction to Machine Learning or B31 Statistical Tests, is preferred.

Single-variable and multi-variable calculus, linear algebra, B36 Real Analysis, A110 Measure Theory, or equivalent.

A114 Functional Analysis and A108 Partial Differential Equations, or equivalent.

Required pass in first theoretical portion of this course, A111 Nonlinear Time series Analysis and Manifold Learning.
Prior deep knowledge of Taken’s theorem-based methods is an absolute prerequisite.

Quantum Mechanics

Undergraduate organic chemistry and/or biochemistry or related chemistry

B11 Classical Electrodynamics, A203 Advanced Optics

Quantum Mechanics

companion course to A203 Advanced Optics

Undergraduate chemistry

Assumes undergraduate organic chemistry or biochemistry.

Basic quantum mechanics and basic concepts of statistics.

Maxwell’s equations in differential form. Solving Maxwell’s equations to obtain electromagnetic waves. Linear algebra of vectors and matrices.

Undergraduate level biochemistry or molecular biology

Maxwell’s equations in differential form. Solving Maxwell’s equations to obtain electromagnetic waves. Quantum mechanics.

Undergraduate level of condensed matter physics

Classical Mechanics and Quantum Mechanics to advanced undergraduate level.

Undergraduate-level knowledge of general chemistry; Advanced-level knowledge of organic chemistry.

Quantum Mechanics and ideally Advanced Quantum Theory. A further background in Quantum Field Theory and Statistical Physics is helpful.

Statistical Physics (B12) or Statistical Mechanics, Critical Phenomena and Renormalization Group (A225); anything equivalent to a basic course on Nonrelativistic Quantum Mechanics.

Undergrad-level quantum mechanics and linear algebra

Linear algebra, probability and statistics. Introductory knowledge of quantum information is helpful, although quantum bits, operations and measurements will be covered here.

Linear algebra, probability and statistics. The student will benefit from introductory knowledge on quantum information, though the exposition will include a short introduction to quantum bits, operations and measurements.

Students must have prior experimental experience in physics or engineering and proficiency in coding (Python, MATLAB, or LabVIEW). Background knowledge in quantum mechanics is beneficial but not required, as the course emphasizes experimental techniques over quantum theory fundamentals.

Cell biology and/or genetics

Cell biology and/or genetics

Neuroscience background required

Advanced undergraduate Cell Biology and Genetics

Introductory neuroscience, computational methods, programming, mathematics.

Background in neuroscience (either at the BSc/MSc level or the OIST basic neuroscience course). Cellular neurophysiology and neuroanatomy.

B46 Introduction to Machine Learning and programming experience in Python, C or C++ are required. Basic calculus of vectors and matrices and differential equations are assumed.

Students should have previously taken at least two basic courses in neuroscience or have completed the equivalent by documented prior learning

Introductory neuroscience and preparation in one or more areas of linear algebra, machine learning, or behavioral ecology is recommended.

Basic knowledge of cellular biology and neurobiology.

Passing “Introduction to Neuroscience” or equivalent is required.

Basic knowledge of cellular biology and neurobiology. Passing “Introduction to Neuroscience” or equivalent is required.

Basic understanding of evolutionary and cell biology at the undergraduate level is assumed. e.g., B27 Molecular Biology of the Cell or B23 Molecular Evolution

Molecular Biology and Genetics required, e.g.: B27 Molecular Biology of the Cell and B35 Genetics and Modern Genetic Technologies

Undergraduate biology, especially evolution. Course B23 Molecular Evolution is required. Contact Prof Sallan if you seek an exemption.

Students are encouraged to have basic knowledge of statistical physics, stochastic dynamics, and machine learning. Basic skills in mathematics, programming, and computer simulations are required.

Basic knowledge of and interest in biology or evolution required, undergraduate biology coursework preferred.

B27 Molecular Biology of the Cell, or equivalent

Students must have at least a basic background in neuroscience.

For this course, a basis in cognitive science (any discipline) is highly advantageous.

Undergraduate mathematics.

A solid background in college-level introductory physics is assumed, therefore a systematic review of elementary mechanics will not be part of this course. Familiarity with certain few basic mathematical concepts is essential. The student should understand Taylor series in more than one variable, partial derivatives, the chain rule, and elementary manipulations with complex variables – say at the level of Advanced Calculus, 2nd Ed, W. Kaplan, Addison-Wesley, 1984 or Calculus and Analytical Geometry, 9th Ed., Thomas & Finney, Addison-Wesley, 1995. Some elementary knowledge of matrices and determinants is also needed – say at the level of Linear Algebra with Applications, 2nd Ed, S. J. Leon, MacMillan 1985 or one of many other equivalent texts at the intermediate level. The student shall have either completed or is concurrently registered in a Mathematical physics course, involving vector analysis, complex variable theory, and techniques for solving ordinary and partial differential equations. However, a thorough grounding in these subjects is not essential and can be picked up during the course.

Undergraduate mechanics and a firm grasp of calculus and vector mathematics

Undergraduate calculus and algebra.

B10 Analytical Mechanics and/or A104 Vector and Tensor Calculus.

B10 Analytical Mechanics and/or A104 Vector and Tensor Calculus.

No prior knowledge assumed

Biology, chemistry, and/or physics at undergraduate levels

Assumes general knowledge in biology

The course is very basic. Non-biology students are welcome.

Familiarity with real and complex numbers will be assumed. Ideally, students will have had some previous exposure to mathematical proofs, though this is not strictly required.

Basic knowledge of elementary mathematics such as differentiation, integration, and elementary linear algebra. However, whenever necessary, mathematical details will be explained.
Students will need to write some code in Python

Undergraduate chemistry

Undergraduate single-variable Calculus or equivalent is required. Multivariable calculus is not a prerequisite. If you are not sure about the prerequisite material, please contact the instructor before enrolling.

There are no prerequisites for this course. Students will be expected to complete assigned readings ahead of class in order to participate fully.

Undergraduate-level coursework in general biology and calculus are recommended but not required.

Curiosity and sense of wonder

No prerequisites. However, without some mathematics and programming background, topics like deep learning are hard to follow.

Basics of calculus, linear algebra, and programming.

Classical mechanics, e.g. B10

Basic skills of computer use.
Familiarity with linear algebra and basic differential equations is assumed, but the course aims to help intuitive understanding of such mathematical concepts by computing and visualization.

undergraduate quantum mechanics and linear algebra

Undergraduate biochemistry, biology, and chemistry

No prerequisite within the OIST graduate syllabus. The expectation is that students have a scientific background, with knowledge equivalent to first-year undergraduate mathematics, or more generally, the equivalent to discrete mathematics taught in many science undergraduate degrees.

Basic probability and statistics; introductory molecular biology; some experience with the command line, Python or R. The course is not suitable for students without any quantitative or biological background.