Course. CPSC 340 / CPSC 532M: Machine Learning and Data Mining
Time and place. MWF 12-1p (Term 1), 4-5p (Term 1), 1-2p (Term 2)
Description. We introduce basic principles and techniques in the fields of data mining and machine learning. These are some of the key tools behind the emerging field of data science and the popularity of the `big data’ buzzword. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We’ll focus on many of the core data mining and machine learning technologies, with motivating applications from a variety of disciplines.
Course. CPSC 524 Computer Graphics: Modeling
Time and place. MF10:30 -12p, Term 2, ICICS/CS 246
Description. Three-dimensional geometric models are the base data for applications in computer graphics, computer aided design, visualization, multimedia, and other related fields. This course will focus on computerized modeling of 3D geometry, and focus on polygonal meshes, the default 3D shape representation. We will study data structures and algorithms for creating, manipulating, editing and analyzing 3D models.
We will also address recent advances in shape modeling interfaces, shape analysis, and fabrication processes such as 3D printing.
Students are expected to have successfully completed an introductory computer graphics course (e.g. UBC CS 314) or have an equivalent background. An existing knowledge of OpenGL is assumed, although knowledge of a comparable system (such as DirectX) should be sufficient.
The course is open to CS, Math, and engineering graduate students.
Course. CPSC 531H: Machine Learning Theory
Time and place. MW 12-1:30p, Term 1, DMP 101
Description. This is a graduate course on some theoretical aspects of machine learning. The emphasis is on foundations and on results with rigorous proofs. The viewpoint is much more computational than statistical.
Course. CPSC 532S: Topics in Artificial Intelligence: Multi-modal Learning with Vision, Language and Sound
Time an place. TTh 11-12:30p, Term 2, DMP 101
Description. Multimodal machine learning is a multi-disciplinary research field which addresses some of the core goals of artificial intelligence by integrating and modeling two or more data modalities (e.g., visual, linguistic, acoustic, etc.). This course will teach fundamental concepts related to multimodal machine learning, including (1) representation learning, (2) translation and mapping, and (3) modality alignment. While the fundamental techniques covered in this course are applicable broadly, the focus will on studying them in the context of joint reasoning and understanding of images/videos and language. In addition to fundamentals, we will study recent rich body of research at the intersection of vision and language, including problems of (i) generating image descriptions using natural language, (ii) visual question answering, (iii) retrieval of images based on textural queries (and vice versa), (iv) generating images/videos from textual descriptions, (v) language grounding and many other related topics. On a technical side, we will be studying neural network architectures of various forms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), memory networks, attention models, neural language models, structures prediction models.
Course. CPSC535P: Digital Humans
Time and place. MW 1:30-3p, DMP 101
Description. This course covers recent advances is building digital representations of humans for a variety of applications, such as product design, character animation, and medicine. The focus is on building realistic models of real humans, using measurements. Topics are organized into 6 modules, building up levels of realism in digital human models. (1) Introduction. Fast-paced overview of the background. General tips on how to critically read and think about research papers, and write reviews. (2) Shape and Motion. Kinematic representations. Skeletons and Skinning. 3D scanning. Statistical models. Reduced coordinate models. (3) Clothing. Representing and assembling garments. Simulating drape. A first look at simulating physics. (4) Soft Bodies. Finite element models. Dynamics. (5) Measurement. 3D shape measurement. Motion capture. Material properties of soft tissues and fabrics. (6) The rest of the story. Topics based on student interest. Examples: human anatomy and physiology; Photorealistic skin rendering
Course. CPSC 540: Machine Learning
Time and place. MWF 4-5p, Term 2, DMP 110
Description. This is a graduate-level course on machine learning, a field that focuses on using automated data analysis for tasks like pattern recognition and prediction. The course will move quickly and assumes a strong background in math and computer science as well as previous experience with statistics and/or machine learning. The class is intended as a continuation CPSC 340 (which is also known as 532M for grad students) and it is strongly recommended that you take CPSC 340 or 532M first before enrolling in CPSC 540. Topics will (roughly) include large-scale machine learning, density estimation, probabilistic graphical models, deep learning, and Bayesian statistics.
Course. CPSC542F / MATH 604: Convex Analysis and Optimization
Time and place. TTh 3:30-5, Term 1, DMP 101
Description. Convex analysis and optimization have emerged as key tools for analyzing and solving a range of computational problems that arise in machine learning, signal and image processing, theoretical computer science, and other fields. It is also forms the backbone for other areas of optimization, including nonconvex problems. The aim of this course is to provide a self-contained treatment of the key ideas in convex analysis and their use in the construction of the building-block algorithms for convex optimization. Topics: fundamentals of convex sets and functions; key convexity-preserving operations and their use in approximation and dualization; geometric and fully-general description of convex duality that includes, for example, Lagrange duality as a special case; basic algorithms such as proximal, splitting, cutting plane, descent, and interior methods.
Earth, Ocean, and Atmospheric Sciences
Course. EOSC 510: Data Analysis in Atmospheric, Earth and Ocean Sciences
Time and place. TTh, 9.30-11a, Term 2, EOS-M 121
Description. This is a course for graduate-level students in the Sciences, in which students will gain quantitative skills for tackling a large range of problems in data analysis and empirical modeling. Although the skills learned in the course are applicable to any field of natural sciences, the course examples are mainly drawn from Earth, Ocean and Atmospheric Sciences. The course will equip students with methods and techniques applicable to a broad range of research problems involving field, experimental, observational and modeled data. The emphasis is on practical applications of data analysis and machine learning methods on actual datasets in order to enable the students to answer a large set of research questions involving ‘big’ data.
Course. EOSC 512: Advanced Geophysical Dynamics
Time and place. TTh 11-12:30, Term 1, EOS-M 107
Description. The purpose of this course is to a) introduce the student to the dynamical principles governing the large-scale low-frequency motions in strongly rotating fluid systems (like the ocean, atmosphere, and liquid planetary core); and b) to develop the skills required to use these principles to solve problems and understand their implications.
Course. MATH 401: Green’s Functions and Variational Methods
Time and place. MWF 12-1p, Term 2, MATX 1100
Course. Math 418/544 Probability I
Time and place. MWF 10-10:50p, Term 1, Math 203
Description. Together with Math 419/545 in term 2, these courses give a comprehensive introduction to mathematically rigorous and measure-theoretic probability theory for honours undergraduates and graduate students. Topics in Probability I include Foundations of Probability, Laws of Large Numbers, Central Limit Theorem, Martingale Theory. The course is intended to be useful for those who use probability as a tool in other fields, or planning to do research in probability. Probability theory has applications in analysis, electrical and computer engineering, statistics, economics, finance, math biology, combinatorics and partial differential equations and has ties to many other fields. Students interested in these fields are encouraged to enroll.
Course. Math 551: Perturbation Methods for Differential Equations
Time and place. MWF 2-3p, Term 2, PCOH 1302
Description. This is a course in modern techniques in applied mathematics, focusing on perturbation methods for partial differential equations. The material provides valuable skills and resources complementary to scientific computations, mathematical modeling in applications, analysis of PDE’s and dynamical systems. The general concepts and methods are illustrated and developed for a wide variety of specific problems arising in math biology, fluid mechanics, materials science, and wave propagation. Prerequisites: Students should have a working knowledge of Applied PDE (such as MATH 400) and of basic Complex Variables (M300).
Course. Math 555 Compressed Sensing
Time and place. TTh 3:30-5:00 (to be changed at the organizational meeting), Term 2
Description. This is a course on mathematics of information. After a review of the classical sampling/signal acquisition paradigm—which is well-established and based heavily on ideas from harmonic analysis—we will discuss sparse and low rank approximation, and compressed sensing (together with related techniques such as matrix completion). We will focus on both mathematical and algorithmic aspects. The course is intended to be self contained. Still, it would be beneficial to have some background in any of the following areas: functional analysis, harmonic analysis, probability on the mathematical side as well as in signal processing, information theory, and optimization on the applied side.
Course. MATH 559: Complex Fluids
Time and place. MWF 2-3p, Term 1, MATX 1206
Description. This course will give students an overview of Non-Newtonian Fluid Dynamics, and discuss two approaches to building constitutive models for complex fluids: continuum modeling and kinetic-microstructural modeling. In addition, it will provide an introduction to multiphase complex fluids and to numerical models and algorithms for computing complex fluid flows.
Course. MATH 564: Evolutionary Dynamics
Time and place. M 10-12p, W 10-11a, Term 1, PCOH 1302
Description. Evolution is the unifying theme in biology. Evolutionary processes are responsible for the emergence of the rich variety of species and behaviours across the planet. Cooperation represents one of the key organizing principles in evolution, and the history of life and of societies could not have unfolded without the repeated cooperative integration of lower level units into higher level entities. This course provides an introduction into mathematical models of evolution and the theory of games. Modeling techniques include: stochastic dynamics of invasion and fixation of mutants in a finite population; evolutionary game theory; adaptive dynamics and the process of diversification and speciation through evolutionary branching; modeling spatially structured populations. Students develop their own research project based on open questions in the literature.
Course. Math 605D: Introduction to Visco-plastic Fluid Mechanics
Time and place. TTh, 9.30-11a, Term 1, FNH 30
Description. This course is intended to give an introduction to the mechanics of simple visco-plastic fluids, e.g., Bingham, Casson, Herschel-Bulkley models. The course audience is applied mathematicians or mathematically oriented engineers at a graduate level with a good grounding in Newtonian fluid mechanics and viscous flows. As well as covering analytical methods and applications to specific types of flow an introduction to numerical methods will be given.
Course. MATH608D: Probability in High Dimensions
Time and place. MWF 10-11, Term 2, Math Annex 1102
Website. http://www.yanivplan.com/math-608d (from previous term)
Description. In the study of probabilistic objects, many surprising, elegant, and useful phenomena occur in the high-dimensional setting (e.g., central limit theorem). We study these phenomena and their applications. We focus on the high-dimensional, but non-asymptotic, regime.
Course. MECH 502 Fluid Mechanics
Time and place. TTh 3:30-5p, Term 1, SWNG 305
Description. Develops graduate level understanding of the fundamental aspects of the physics of fluid flow including: tensor calculus; derivation of conversation laws in fluid mechanics; the stress tensor and constitutive laws; exact solutions of the Navier-Stokes equations; Stokes flow; potential flow; boundary layers; hydrodynamic stability and the transition to turbulence.
Course. MECH 510: Computational Methods in Transport Phenomena I (Term 1)
Time and place. MWF 1-2p, Term 1, Food Nutrition and Health Bldg, Rm 320
Description. Analytical, computational, and experimental methods in fluid mechanics. Overview of CFD program development. Finite volume methods, spacial discretization and spatial accuracy analysis. Boundary conditions. Time advance methods, time accuracy, and stability. Application to model problems and to the incompressible laminar Navier-Stokes equations. Validation techniques for CFD codes. This course is not eligible for Credit/D/Fail grading.
Course. MECH 511 (3) Computational Methods in Transport Phenomena II (Term 2)
Time and place. M 11-12:30, Term 2, Civil and MECH 1210
Description. Selected advanced topics in CFD, typically chosen from: Finite volume methods on curvilinear meshes and structured mesh generation. Finite volume methods on unstructured meshes. Multigrid methods for elliptic PDE’s. Reynolds-averaged form of the Navier-Stokes equations and turbulence modeling. Three-dimensional flows. Compressible flows. This course is not eligible for Credit/D/Fail grading. Prerequisite: MECH 510