# Courses: past

The following courses were scheduled for the past academic year:

The following courses were scheduled for the past academic year:

Bojan Mohar : mohar@sfu.ca

Simon Fraser University

Undergraduate course in graph theory

Undergraduate course on (discrete) probability

Linear algebra

Registration for this course is not currently available.

The course will provide an introduction to algebraic and probabilistic techniques in combinatorics and graph theory. The main topics included will be: Eigenvalues of graphs and their applications, probabilistic methods (first order, second order, Lovasz local lemma), Szemeredi regularity lemma. Recent discoveries like the proof of the Sensitivity conjecture, the use of eigenvalues for equiangular lines, etc., will be part of the course. '

- Time: Tuesday 10:30-12:20 and Thursday 10:30-12:20
- First day of classes: January 9
- Reading break: February 20-25
- Last day of classes: April 11

Note: This course is also offered through PIMS and WDA (Western Dean’s Agreement) as an online course. A Zoom link will be shared with registered students.

- Introduction (warmup application of graph eigenvalues)
- Eigenvalue basics (including Perron-Frobenius Theorem)
- Eigenvalue interlacing (bounds on the maximum clique and chromatic number)
- Wilf’s Theorem, proof of sensitivity conjecture
- Graph Laplacians (Matrix-tree Theorem, Cheeger inequality)
- Random walks, effective resistance
- Spectral sparsifiers

- Random graphs, probabilistic method (including Lovasz local lemma)
- Quasirandom graphs
- Eigenvalues of random graphs (Wigner, Tao-Vu)
- Regularity Lemma
- Finding regular partitions
- Random covers and Ramanujan graphs

- Homework assignments 30%
- Midterm 30%
- Final exam 40%

Lior Silberman : lior@math.ubc.ca

University of British Columbia

Galois Theory

Basic number theory

Introductory algebra (groups, rings, modules, polynomial rings, UFD and PID).

Commutative algebra is useful but not required.

Registration for this course is not currently available.

This will be a standard graduate number theory course. Topics will include:

- Number fields, rings of integers, ideals and unique factorization. Finiteness of the class group.
- Valuations and completions; local fields.
- Ramification theory, the different and discriminant.
- Geometry of numbers: Dirichlet’s Unit Theorem. and discriminant bounds.
- Other topics if time permits

The main pre-requisites are basic algebra (rings and fields, rings of polynomials, unique factorization in Euclidean\ndomains), basic number theory (modular arithemtic, factorization into primes) and Galois Theory, but no specific courses are required.

https://personal.math.ubc.ca/~lior/teaching/2324/538_W24/

Lectures will take place every Wednesday and Friday from 10:00am-11:30am (Pacific Time).

Lectures will be shared via zoom. Students will need to have installed the zoom app on their desktop/laptop.

Michael Monagan : mmonagan@sfu.ca

Simon Fraser University

An undergraduate degree in mathematics and basic programming skills (you are comfortable programming with arrays and loops and writing subroutines). Or an undergraduate degree in computer science and an algebra course (in groups or rings and fields, or number theory).

Registration for this course is not currently available.

A course on algorithms for algebraic computation and tools for computing with multivariate polynomials, polynomial ideals, exact linear algebra, and algebraic numbers. Tools include the Fast Fourier Transform, Groebner bases, and the Schwartz-Zippel Lemma. We will use Maple as a calculator and as a programming language to implement algorithms. Instruction in Maple usage and programming will be provided.

Lectures are on Tuesdays and Thursdays 9:30am to 11:20am (Pacific Time) .

Lectures will be shared via zoom. Students will need to have installed the zoom app on their desktop/laptop.

Anthony Quas : aquas@uvic.ca

University of Victoria

A course on measure theory.

Registration for this course is not currently available.

Ergodic theory is the study of measure-preserving transformations. These occur naturally in an array of areas of mathematics (e.g. probability, number theory, geometry, information theory). The course will introduce measure-preserving transformations, give a range of basic examples, prove a number of general theorems (including the Poincare recurrence theorem, the Birkhoff ergodic theorem and sub-additive ergodic theorem). Entropy, one of the principal invariants of ergodic theory will be introduced. From there, the course will focus on applications to other areas.

Lectures will take place every Monday and Thursday from from 8:30-9:50 (Pacific time).

Lectures will be shared via zoom. Students will need to have installed the zoom app on their desktop/laptop.

Adam Topaz : topaz@ualberta.ca

University of Alberta

There are no strict mathematical prerequisites, but a certain level of mathematical maturity will be assumed (see the syllabus for more details). Although not strictly required, it would be useful for students to have some

*minor*level of familiarity with interactive theorem proving, for example at the level of the natural number game

Registration for this course is not currently available.

The last few years have seen amazing advances in interactive proof assistants and their use in mathematics. For example, Lean’s mathematics library `mathlib`

now has over one million lines of code and is still growing in a significant rate. Furthermore, recent highly celebrated successes in the subject, such as the completion of the *sphere eversion project* and the *liquid tensor experiment*, suggest that we are approaching a paradigm shift in mathematics, where cutting edge research can be formally verified in a relatively short amount of time. This course will serve as an introduction to the formalization of mathematics, using the Lean4 interactive proof assistant and its mathematics library `Mathlib4`

. See the attached syllabus for an outline of the topics we expect to cover.

Lectures are Tuesdays and Thursdays, 11am to 12:20pm, Mountain time. All lectures will take place electronically using zoom (or similar software).

James D. Lewis : lewisjd@ualberta.ca

University of Alberta

Students should have taken a course on algebraic geometry. It is helpful to know some differential geometry, particularly how it applies to complex manifolds, de Rham and Betti (singular) cohomology. Some exposure to homological algebra will be useful.

Registration for this course is not currently available.

Students taking this course will be exposed to the latest developments in the field of regulators algebraic cycles. This course was taught to advanced graduate students and experts alike at the University of Alberta in 2013. It was later taught at the University of Science and Technology in China, in 2014. A *detailed* syllabus can be extracted from the table of contents of the uploaded pdf file.

Lectures will take place on Mondays, Wednesdays and Fridays from 13:00-13:50 (Mountain Time)

These lectures will take place via zoom. Students should have zoom installed on their laptop or other device.

Thomas Hillen : thillen@ualberta.ca

University of Alberta

Some basic knowledge on partial differential equations.

Registration for this course is not currently available.

In this course we will study the theory of hyperbolic systems of conservation laws.

Hyperbolic systems arise in many areas of applied mathematics, including gas dynamics, thermodynamics, population dynamics, or traffic flow. In contrast to dissipative systems (like reaction-diffusion equations), solutions of hyperbolic systems with smooth initial data can generate “shocks” in finite time. The solution is no longer differentiable and weak solutions have to be studied.

We will develop the existence and uniqueness theory for solutions of conservation laws in spaces of functions of “bounded variation" (BV-spaces). At the beginning we will recall distributions and weak limits of measures. Then we study “broad” solutions (solutions which do not form shocks). After that we investigate discontinuous solutions in detail, we will derive the Rankine-Hugoniot conditions, the entropy conditions, the Lax-condition and we will discuss the vanishing viscosity method. We will classify strictly hyperbolic systems into genuinely nonlinear or linear degenerate systems. Then we use solutions to the Riemann problem to define a front tracking algorithm. This method is merely an\ analytical tool to obtain results on local and global existence and on uniqueness.

Lectures will take place Monday, Wednesday and Friday from 13:00-13:50 (Mountain Time).

Lectures are online on zoom.

Julien Arino : Julien.Arino@umanitoba.ca

University of Manitoba

Permission of the department. The course is dual listed, the undergraduate version requires a second year linear algebra course. While the prerequisites are low, you should be comfortable with the content of a solid second year linear algebra course, as the course is fast paced.

Registration for this course is not currently available.

Matrices are ubiquitous in many aspects of mathematics. They show up, for instance, when considering the local asymptotic stability of equilibria of systems of ordinary differential equations, the long term behaviour of Markov chains, the study of graphs and the discretization of reaction-diffusion equations.

- explore the role of matrices in several fields of mathematics;
- study properties of these matrices;
- develop a toolbox to study some matrix properties computationally.

https://julien-arino.github.io/math-4370-7370/

For more information about this course, including a detailed syllabus, please see the course website.

Alia Hamieh : alia.hamieh@unbc.ca

University of Northern British Columbia

A graduate course in analytic number theory that includes Dirichlet series and a complex-analytic proof of the prime number theorem (preferably Analytic Number Theory I taught by Kadiri in Fall 2022)

Registration for this course is not currently available.

This course is an advanced graduate course in number theory, designed to follow Analytic Number Theory I taught by Prof. Habiba Kadiri (University of Lethbridge) in Fall 2022 and Analytic Number Theory II taught by Prof. Greg Martin (UBC) in Winter 2023. All three of these courses are part of the current PIMS CRG “L-functions in Analytic Number Theory”. In this course, we will establish estimates for moments of L-functions and explore the tools needed to study them including approximate functional equations, zero density estimates, zero free regions, mean value estimates for Dirichlet polynomials, large sieve inequalities, Poisson and Voronoi summation formulae, shifted convolution sums, holomorphic modular forms and associated L-functions, trace formulae, and the spectral theory of automorphic forms.

Soumik Pal : soumikpal@gmail.com

University of Washington

Young-Heon Kim : yhkim@math.ubc.ca

University of British Columbia

First year graduate course in real analysis and/or probability.

Some knowledge in PDE and differential geometry at a graduate level will be very helpful.

Registration for this course is not currently available.

The space of probability distributions with finite second moments can be made into a natural metric space, called the Wasserstein space, whose metric is defined by using the optimal transportation between probability distributions. On this metric space one can draw curves that represent motion along the steepest descent (AKA gradient flow) of functionals of probability measures. This is a very fruitful way to view many important families of probability measures that arise from PDEs and stochastic processes. For example, using this geometric framework, one may derive functional inequalities and infer rates of convergence of Markov processes. A striking example is that of the heat equation, whose solution can be interpreted as the family of marginal distributions of Brownian motion. In the Wasserstein space, this curve of probability laws is the gradient flow of the Shannon entropy.

We will discuss the theory of Wasserstein gradient flows, including the formal Riemannian calculus due to Otto, and the modern techniques of metric measures spaces. Apart from the classical examples, we will also discuss many modern variations such as Wasserstein mirror gradient flows that come up in statistical applications. A fruitful interaction between probability, geometry, and PDE theory will be developed simultaneously. This is a continuation of the sequence of OT+X courses under the Kantorovich Initiative.

The course is being offered simultaneously at Korea Advanced Institute of
Science and Technology (KAIST) and the PIMS network, including the University of
Washington, Seattle. **Due to different time schedules for individual campuses
and
the time zones, the course has an unusual structure. Please read the details
below carefully.**

Lecture hours

**6:30pm - 8pm Pacific on Tuesdays and Thursdays**. Thus we will have two classes per week, each for 90 mins.Lectures will be taught over Zoom and videos and notes will be made available to everyone afterwards.

A Slack channel will be used to communicate with students and distribute teaching material.

There will be no exams in this course. Occasional homework problems will be provided.

Students at Canadian PIMS Member Universities may register through the Western Deans Agreement. Students at UW may register directly for the UW course. Course codes and other registration details for students in either of these cases are listed in the registration section above. Students at other institutions should contact one of the instructors to attend the course as a non-registered student.

Part I is a recap of the basics of Monge-Kantorovich optimal transport theory.
*You do NOT need to take this part if you are already familiar with the basics*.
This will be covered between **AUG 28** and **SEP 26**. Topics covered during
this period are:

- linear programming
- Monge-Kantorovich problem
- Kantorovich duality
- Monge-Ampère PDE
- Brenier’s Theorem
- Wasserstein-2 metric

This will start on **SEP 27** and continue through **DEC 7**. A rough syllabus
of topics covered are presented below in the order they will be covered. There
might be some changes depending on our progress.

- Wasserstein space
- metric property
- geodesics, displacement interpolation, generalized geodesic
- Geodesic convexity

- AC curves in the Waserstein space and the continuity equation
- Benamou-Brenier and dynamic OT
- Otto calculus
- tangent spaces to the Wasserstein space
- Riemannian gradient

- Diffusions as gradient flows via Otto calculus
- Brownian motion
- Langevin diffusions

- log-Sobolev and other functional inequalities
- Convergence of finite dimensional gradient flow of particles to the
**McKean-Vlasov diffusions**and gradient flow in the Wasserstein space. - The implicit Euler or JKO scheme
- Entropy regularization and gradient flows
- Schrödinger bridges
- Large deviation and gradient flows

- Mirror gradient flows, parabolic Monge-Ampere and the Sinkhorn algorithm

Shaun Lui & Mikael Slevinsky : Shaun.Lui@umanitoba.ca

University of Manitoba

Undergraduate analysis and PDEs

Some exposure to numerical analysis is desirable but not necessary

Some homework questions will require computer programming (MATLAB, Julia or similar)

Permission of Instructor

Registration for this course is not currently available.

Spectral methods are numerical methods for solving PDEs. When the solution is analytic, the convergence rate is exponential. The first part of this course gives an introduction to spectral methods. The emphasis is on the analysis of these methods including truncation and interpolation error estimates, and condition number estimates. The second part of the course focuses on fast algorithms for orthogonal polynomials. These algorithms leverage data-sparsities that are present in many of the problems when solved by orthogonal polynomial expansions.

- Trigonometric and orthogonal polynomials (truncation and interpolation error estimates, aliasing, Lebesgue constants)
- Fourier spectral (FFT), spectral Galerkin and spectral tau methods
- Spectral collocation for Poisson equation with Dirichlet BCs (convergence and condition number estimates)
- Neumann problems and fourth-order PDEs
- Other topics (Ultraspherical spectral methods, time-dependent PDEs)

- Synthesis and analysis
- Chebyshev polynomials and the fast discrete sine and cosine transforms
- Modification algorithms for orthogonal polynomials (d) Fast approximation of the connection coefficients
- Multivariate orthogonal polynomials via Koornwinder’s construction (f) Time evolution with exponential integrators

- Tues, Thurs 3 - 4:15 (CDT)

- MH416 and Zoom

- Course notes will be provided.

- J. Shen T. Tao and L.-L. Wang, Spectral methods. Algorithms, analysis and applications, Springer, 2011.
- L.N. Trefethen, Spectral Methods in Matlab, SIAM, 2000.
- L.N. Trefethen, Approimation Theory and Approximation Practice (Extended Ed.), SIAM, 2020.
- S. Olver, R. M. Slevinsky, and A. Townsend, Fast algorithms using orthogonal
polynomials,
*Acta Numerica*, 29: 573–699, 2020.

There are 4 Homeworks (each contributing 17% toward the grade) and a project (32%).

The Department of Mathematics, the Faculty of Science and the University of Manitoba regard acts of academic dishonesty in quizzes, tests, examinations or assignments as serious offenses and may assess a variety of penalties depending on the nature of the offense. Acts of academic dishonesty include bringing unauthorized materials into a test or exam, copying from another student, plagiarism and examination personation. Students are advised to read section 7 (Academic Integrity) and section 4.2.8 (Examinations: Personations) in the “General Academic Regulations and Requirement” of the current Undergraduate Calendar. Note, in particular that cell phones and pagers are explicitly listed as unauthorized materials, and hence may not be present during tests or examinations. Penalties for violation include being assigned a grade of zero on a test or assignment, being assigned a grade of “F” in a course, compulsory withdrawal from a course or program, suspension from a course/program/faculty or even expulsion from the University. For specific details about the nature of penalties that may be assessed upon conviction of an act of academic dishonesty, students are referred to University Policy 1202 (Student Discipline Bylaw) and to the Department of Mathematics policy concerning minimum penalties for acts of academic dishonesty. The Student Discipline Bylaw is printed in its entirety in the Student Guide, and is also available on-line or through the Office of the University Secretary. Minimum penalties assessed by the Department of Mathematics for acts of academic dishonesty are available on the Department of Mathematics web-page. All Faculty members (and their teaching assistants) have been instructed to be vigilant and report incidents of academic dishonesty to the Head of the Department.

Li Xing : lix491@usask.ca

University of Saskatchewan

Students have taken undergraduate courses in linear regression and have basic R skills.

Registration for this course is not currently available.

Based on a mathematical and statistical theory foundation, the course introduces statistical methods for supervised and unsupervised learning, focusing on hands-on skills with statistical software, R, and applications to real data. The course covers resampling methods, regression and classification, tree-based methods, dimension reduction and clustering. It embeds R training throughout the entire class.

**Lecture Section**: Wednesday 6:00pm-9:00pm CST (online via zoom)**Lab Section**: Thursday 3:30pm-4:50pm CST (online via zoom for outside USask students, or onsite in a lab room to be announced).**Office Hours**: Friday 5:00pm-6:00pm CST, and by appointment (online via zoom, or onsite by appointment)

Please see the syllabus document for more details, including required reading, learning objective and evaluation components.

Clifton Cunningham : ccunning@ucalgary.ca

University of Calgary

Undergraduate linear algebra, abstract algebra (groups, rings, fields)

multivariable calculus and algebraic number theory

A course in modules would be helpful, but not necessary

A course in classical commutative algebra is not required

Registration for this course is not currently available.

The objective of this course is to provide an introduction to modern algebraic geometry in the language of schemes, with an emphasis on arithmetic schemes, integral models and applications to L-functions, and resolutions of singularities. The course also introduces the etale site on varieties, and sheaves on this site.

Joshua Zahl : jzahl@math.ubc.ca

University of British Columbia

This course assumes graduate-level background in measure theory, real analysis, and harmonic analysis (i.e. at the level of Math 420/507 and Math 404/541 at UBC).

Registration for this course is not currently available.

We will cover the advances in decoupling theory beginning with Bourgain and Demeter’s 2014 proof of the $l^2$ decoupling conjecture. We will also cover Fourier restriction theory, and in particular the recent use of tools such as the polynomial method.

Lectures will take place every Monday, Wednesday and Friday 11:00am-12:00pm (Pacific Time)

Alexander Giessing : giessing@uw.edu

University of Washington

Jiahua Chen : jhchen@stat.ubc.ca

University of British Columbia

The course assumes that the students have a taken classes in advanced theoretical statistics comparable to PhD level courses STAT 581, 582, 583 at University of Washington. Some knowledge of measure theoretic probability will be helpful, too.

Registration for this course is not currently available.

In this course we develop elements of the theory of Gaussian and empirical processes that have proved useful for statistical inference in high-dimensional models, i.e. statistical models in which the number of parameters is much larger than the sample size. The course consists of three parts, with the first two parts laying the foundation for the third one: an introduction to modern techniques in Gaussian processes, a recap of empirical classical process theory emphasizing weak convergence on metric spaces, and lastly, a discussion of Gaussian approximation, high-dimensional CLTs, and the conditional multiplier bootstrap.

- Part 1: Elements of Gaussian processes (concentration, comparison, anti-concentration, and super-concentration inequalities, Talagrand’s Generic chaining bounds).
- Part 2: Elements of empirical processes (convergence of laws on separable metric spaces, Glivenko-Cantelli and Donsker theorems under metric and bracketing entropy, applications to bootstrap)
- Part 3: A selection of theoretical problems in high-dimensional inference (including but not limited to Gaussian approximation, high-dimensional CLTs, and multiplier bootstrap when function classes are not Donsker).

There will be regular homework assignments and an oral examination. The oral examination will work as follows: The lecture will be divided in roughly ten topics which will be shared with the students ahead of time. At the day of the examination the students will randomly draw two topics and give two 10-15 min presentations on their topics on the blackboard (no prepared notes allowed). Each presentation will conclude with ca. 5 minutes of follow-up questions. Textbooks for the first and second part:

- Dudley, R. M. (2014). “Uniform Central Limit Theorems”. CUP.
- Giné, E. and Nickl, R. (2016). “Mathematical Foundations of Infinite-Dimensional Statistical Models”. CUP.
- van der Vaart, A. and Wellner, J. (1996). “Weak Convergence and Empirical Processes”. Springer.

Typed lecture notes of all three parts will be provided.

*Please note, the WDA registration deadline for this course at UBC will be Jan
6th, 2023.*

Alireza Nojeh : alireza.nojeh@ubc.ca

University of British Columbia

The course does not have formal prerequisites, but assumes background knowledge of linear algebra and calculus, including some degree of vector calculus, at the undergraduate engineering/physics level. Familiarity with the calculus of variations, as well as basic quantum mechanics, would also be helpful, but those are not formal requirements since the key necessary concepts will be presented.

Registration for this course is not currently available.

This course provides an extensive theoretical foundation for as well as hands-on introduction to several widely used methods for studying the properties of materials and structures, in particular at the nanoscale and mesoscale. The majority of the time is spent on quantum-mechanical methods: the first-principles approaches (starting from the Hartree-Fock theory and building up to Configuration Interaction and the Møller–Plesset Perturbation Theory) and, in particular, the Density Functional Theory, which are derived and discussed in detail. Semi-empirical methods such as Tight Binding and Molecular Dynamics are also covered, as well as strategies for modelling material properties (electronic, mechanical, optical, etc.). Practical activities include implementing some of the above theories in computer code, in addition to using established software (Gaussian, SIESTA, VASP, LAMMPS, etc.). Each student also works on a project of their choice using the methods discussed.

- Modelling quantum systems and phenomena
- The many-body wave function and the Schrödinger equation
- The Born-Oppenheimer approximation
- Spin and the Pauli exclusion principle
- Representation of functions

- Hartree products and Slater determinants
- The variation principle
- The expectation value of the Hamiltonian with a single Slater-determinant
- Lagrange’s method of undetermined multipliers
- Exchange interaction, the Fock operator, and the Hartree-Fock equations

- Unitary transformations and the diagonalization of the Hartree-Fock equations
- The Koopmans theorem and the significance of canonical Hartree-Fock orbitals

- Basis functions and basis sets
- The Roothaan equations
- Mulliken population analysis

- Many-electron excitations
- Basis set for many-electron wave functions
- Configuration interaction
- The Møller-Plesset perturbation theory

- Functional derivatives
- The theorems of Hohenberg and Kohn
- The Kohn-Sham method
- Total energy in DFT, and the significance of Kohn-Sham orbitals
- Correlation energy and exchange-correlation functionals
- The connection between DFT and the Thomas-Fermi-Dirac and Hartree-Fock theories
- Periodicity, the Bloch theorem, and band structure in DFT
- Finite-temperature DFT
- Time-dependent DFT

- Linear combination of atomic orbitals
- The Hückel method
- The Pariser-Parr-Pople method
- The tight-binding method

- Molecular mechanics and molecular dynamics
- Force fields
- Time propagation
- Temperature, pressure, thermostats, and barostats

*Please note, the WDA registration deadline for this course at UBC will be Jan
6th, 2023.*

Habiba Kadiri : habiba.kadiri@uleth.ca

University of Lethbridge

Elementary Number Theory

Real and Complex Analysis

Registration for this course is not currently available.

This is a first course in analytic number theory. In this course we will focus on the theory of the Riemann zeta function and of prime numbers. The goal of this course will include proving explicit bounds for the number $\pi(x)$ of primes which are less than a given number $x$. Building analytical tools to prove the prime number theorem (PNT) will be at the core of this course. We will explore and compare explicit formulas, whether they are using smooth weights or a truncated Perron formula, to relate averages over primes and $\pi(x)$ to sums over the zeros of zeta. Another originality of this course will be to explore each topic explicitly (essentially by computing all the hidden terms implied in the asymptotic estimates). With this respect, students will get an introduction to relevant programming languages and computational software. This will be closely connected to Analytic Number Theory 2 by Greg Martin (UBC), as the sequences of topics are coordinated between us; the intention is for students at all PIMS institutions to be able to take the second analytic number theory course as a continuation of the first one with maximum benefit. In addition, these two courses will provide excellent training for students who plan to attend the “Inclusive Paths in Explicit Number Theory” CRG summer school in 2023. All these events are part of the PIMS CRG “L-functions in Analytic Number Theory” (2022-2025).

Lior Silberman : lior@math.ubc.ca

University of British Columbia

There will be no formal pre-requisites. Ideally students would have a general graduate background including real analysis and integration, point set topology, and functional analysis. Familiarity with the classification of complex semisimple Lie algebras (e.g. by taking UBC MATH 534) would be an advantage.

Registration for this course is not currently available.

This is a graduate course on the structure and representation theory of real Lie groups. The course will have four parts: an introduction to topological and compact groups, the basics of Lie groups and differential geometry, the structure and representation theory of compact Lie groups, and (as time allows) the structure and representation theory of semisimple Lie groups.

https://personal.math.ubc.ca/~lior/teaching/2223/535_W23/

*Please note, the WDA registration deadline for this course at UBC will be Jan
6th, 2023.*

Geoff Schiebinger : geoff@math.ubc.ca

University of British Columbia

Linear algebra as in Math 307

Registration for this course is not currently available.

The course covers foundational mathematical tools that are useful in analyzing high-dimensional single-cell datasets, and modelling developmental stochastic processes. We cover basic probability theory, statistical inference, convex optimization, Markov stochastic processes, and advanced topics in optimal transport.

See the course website for the syllabus and other details.

https://sites.google.com/view/math612d/home

Li Xing : lix491@usask.ca

University of Saskatchewan

Students should have basic statistical theoretical knowledge

A good understanding of linear regression

Basic R coding skills.

Registration for this course is not currently available.

The course provides learning opportunities on statistical software, R, with some focus on data management and wrangling, reproducibility, and visualization. On top of that, there are basic introductions to Machine Learning such as k-NN, Naive Bayes, regression methods, etc. The focus is on hands-on skills with R and applications to real data.