This course will give a broad overview of the field of quantum computing. We will start with a crash course in the fundamentals of quantum computing (qubits, quantum circuits, basic quantum algorithms such as Grover's search algorithm and Shor's factoring algorithm). Armed with the basics, we will then explore topics at the frontier of quantum computing: quantum complexity theory, device-independent quantum cryptography, quantum machine learning algorithms, and quantum supremacy. Students will make project presentations at the end of the course. This is a theoretical course that requires mathematical maturity and a strong background in linear algebra and probability theory. Familiarity with analysis of algorithms and complexity theory is a major plus but not required. No physics background will be assumed.

Computational linguistics and the processing of language by computer. Topics include: language models; context-free grammars; chart parsing, statistical parsing; semantics and semantic interpretation; ambiguity resolution techniques; reference resolution. Emphasis on statistical learning methods for lexical, syntactic, and semantic knowledge.

Representing knowledge symbolically in a form suitable for automated reasoning, and associated reasoning methods. Topics from: first-order logic, entailment, the resolution method, Horn clauses, procedural representations, production systems, description logics, inheritance networks, defaults and probabilities, tractable reasoning, abductive explanation, the representation of action, planning.

Introduction to vision, visual processes, and image understanding. Camera system geometry and image acquisition. Scene lighting and reflectance models. Image and object classification. Generative models. 3D vision. Temporal sequence/video analysis. Combining vision with other modalities.

Identification and characterization of the objects manipulated in computer graphics, the operations possible on these objects, efficient algorithms to perform these operations, and interfaces to transform one type of object to another. Display devices, display data structures and procedures, graphical input, object modelling, transformations, illumination models, primary and secondary light effects; graphics packages and systems. Students, individually or in teams, implement graphical algorithms or entire graphics systems.

An introduction to probability as a means of representing and reasoning with uncertain knowledge. Qualitative and quantitative specification of probability distributions using probabilistic graphical models. Algorithms for inference and probabilistic reasoning with graphical models. Statistical approaches and algorithms for learning probability models from empirical data. Applications of these models in artificial intelligence and machine learning.

The course examines how fundamental building blocks of data systems such as indexing, query processing, execution and optimization are influenced by Machine Learning. We will cover the fundamentals of instance optimization both for relational and information retrieval systems: these include workload adaptive indexing, neural retrieval and ranking, workload-based optimization strategies for query execution as well as instance-optimized performance prediction and infrastructure sizing.

Introduction to techniques involving natural language processing and speech in applications such as information retrieval, speech recognition and synthesis, machine translation, summarization, and dialogue. N-grams, corpus analysis, neural methods, and information theory. Python and other software.

Many problems in Computer Science can be represented as instances of propositional reasoning problems. For example, any problem in NP can be represented as a SAT problem (Boolean Satisfiability) since SAT is complete for the class NP. For many problems, their representation in SAT is very natural, and more importantly, can often be effectively solved by a general purpose SAT solver. This means that instead of developing and implementing a problem specific algorithm, we can often solve our problems by the much simpler device of encoding them into SAT and then using a SAT solver. Surprisingly, the SAT solver can often outperform problem specific algorithms.

In this course you will be introduced to the basic algorithms that are used to SAT and other types of propositional reasoning problems. In addition, we will discuss various encoding techniques for translating various problems into SAT — the encoding used can have a dramatic effect on performance. Besides problems that can be encoded into SAT we often need to deal problems that require some form of optimization, or some form of quantification (e.g., to reason about two person games). Such problems can be encoded as MAXSAT problems, or QBF problems. We will also cover algorithms for solving these kinds of propositional problems.

The aim of the course is to provide you with the background needed to exploit modern SAT–MAXSAT–QBF solvers in your own work. Knowledge of the basics of propositional logic; be familiar and comfortable with programming, data structures and algorithms.

Understanding human behaviour as it applies to user interfaces: work activity analysis, observational techniques, questionnaire administration, and unobtrusive measures. Operating parameters of the human cognitive system, task analysis and cognitive modelling techniques, and their application to designing interfaces. Interface representations and prototyping tools. Cognitive walkthroughs, usability studies, and verbal protocol analysis. Case studies of specific user interfaces.

Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour manually. This course introduces the main concepts and ideas in ML and provides an overview of many commonly used machine learning algorithms. It also serves as a foundation for more advanced ML courses.

The students will learn about ML problems (supervised, unsupervised, and reinforcement learning), models (linear and nonlinear, including neural networks), loss functions (squared error, cross entropy, hinge, exponential), bias and variance trade-off, ensemble methods (bagging and boosting), optimization techniques in ML, probabilistic viewpoint of ML, etc.

An introduction to neural networks and deep learning. Backpropagation and automatic differentiation. Architectures: convolutional networks and recurrent neural networks. Methods for improving optimization and generalization. Neural networks for unsupervised and reinforcement learning.

An introduction to the principal mathematical models of sentence structure used in computational linguistics today. Topics include: string matching and similarity, string and tree transducers, extended context-free formalisms, tree-adjoining grammar, substructural logics, discourse representation calculi, typed feature structures, and topological models. Parsing, algorithmic complexity, algebraic properties, and formal equivalence will be discussed. A basic knowledge of logic, formal language theory, and graph theory is required. Some familiarity with syntactic theory will be helpful, but is not assumed.

This is a graduate course broadly on topics of speech processing by machine including digital signal processing, automatic speech recognition, and speech synthesis. systems. Topics include: articulatory and acoustic phonetics, prosody and information structure, introduction to digital signal processing of speech, automated speech recognition, text-to-speech synthesis, language models, dialogue modelling and dialogue systems.

The class is aimed at preparing students for working with geometric data via understanding fundamental theoretical concepts. Extending traditional signal processing, geometry processing interprets three-dimensional curves and surfaces as signals. Just as audio and image signal data can be filtered, denoised, and decomposed spectrally, so can the geometry of a three-dimensional curve or surface. In this course, we study the algorithms and mathematics behind fundamental operations for interpreting and manipulating geometric data. These essential tools enable: geometric modeling for computer aided design, life-like animations for computer graphics, reliable physical simulations, and robust scene representations for computer vision.

Topics include: discrete curves and surfaces, curvature computation, surface reconstruction from point clouds, surface smoothing and denoising, mesh simplification, parameterization, symmetry detection, shape deformation and animation.

This course will cover advanced aspects of graphics and computer animation. It will be offered in a seminar-style format involving readings of academic papers, student-led presentations, and a course project.

This course will cover interactive computing and technologies from the perspective of human-computer interaction. It will be offered in a seminar-style format involving readings of academic papers, student-led presentations, and a course project.

This is a research topics course in database management. Topics will vary from year to year.

This course will cover how computing technology is moving beyond desktop computers and becoming increasingly integrated into everyday environments. It will be offered in a seminar-style format involving readings of academic papers, student-led presentations, and a course project.

The software and internet industries; principles of operation for successful software enterprises; innovation and entrepreneurship; software business definition and planning; business models, market and product planning; product development, marketing, sales, and support; financial management and financing of high-technology ventures; management, leadership, and partnerships. Students will all write business plans in teams.

Introductory course covering the foundations of computational imaging. Topics include basic image processing, convolutional neural networks for image processing, digital photography, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, and end-to-end optimization of optics and imaging systems. Emphasis on applications and solving inverse problems using classic algorithms, formal optimization, and modern machine learning techniques.

This course covers several topics in machine learning theory. The first half focuses on uniform convergence-based methods (e.g., covering, chaining) to establish generalization through complexity measures like Rademacher complexity and VC dimension. Second half starts with reproducing kernel Hilbert spaces and demonstrates double descent phenomenon in a kernel ridge regression setup. Finally, we discuss linearization (NTK) and feature learning in neural networks.

This will be a seminar-style course, covering a combination of seminal work and recent advances in computer science education and related fields, drawing on methods and theories from Computer Science Education, Learning Sciences, Human-Computer Interaction, and Educational Technology. Students will conduct weekly readings, with student led presentations and discussions each week. A final project will make up a large part of the grade, while student presentation skills and participation in class and in readings will also be emphasized. Students will learn how to design and evaluate interactive learning systems and educational technologies, and how to critically examine and evaluate both qualitative and quantitative research studies in CS Education.

This course will study techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in learning about cutting edge research in the field. Students will conduct reading and critical analysis of scientific research papers, that will be discussed in class. A final project will make up most of the grade, while student presentation and critical analysis skills will also be emphasized.

An advanced seminar course on selected topics in computer vision and computational imaging. Topics will be based on a collection of papers from the current literature as class reading.

Computational cognitive modeling is an increasingly key aspect of artificial intelligence (AI). As AI is deployed in systems that touch every aspect of people's life and work, modeling of intelligent behaviour needs to be cognizant of human ways of thinking and knowing. This is especially true in the area of language, where successful communication depends on an AI having linguistic representations compatible with human expectations. Research in computational linguistics and in cognitive modeling of language has consequently seen a growth in fruitful exchange of ideas and technologies. In this course, students will learn about key computational models for semantics and pragmatics of language that draw on cognitive theories, as well as cognitive approaches that benefit from recent advances in machine learning. This seminar will have weekly reading assignments and in-class discussion sessions interspersed with hands-on computational modeling. Students will develop their own project proposal or select one recommended by the instructor. The course grade will be based on weekly reading responses, participation in class discussion, a class presentation, a project proposal, and a final project.

This course will involve discussion of recent developments in machine learning based on discussions of research papers. Topics may involve deep learning and its applications, foundation models, ethical, societal, and safety implications of ML, and questions of efficiency and scalability.

This is a seminar course that explores recent advances in knowledge representation an reasoning. The course draws predominantly on research readings. The format of the course is a mix of class lectures, seminars, and student paper presentations. Students are typically required to complete a course project.

This course will involve a discussion of several recent developments in machine learning research based on a collection of ML research papers. Topics may include basic learning algorithms, representation learning, ML theory, and application specific aspects of ML research. The course can involve case studies of contemporary ML problems and pipelines.