Get ready to explore the future of high-performance computing (HPC) and artificial intelligence (AI) and its influence on the way we live, work and learn, with the HPC-AI Summer Seminar Series by Stanford High-Performance Computing Center and the HPC-AI Advisory Council. This 1-unit course is designed to provide practical insights and thought leadership and discuss topics of great societal importance.

This course provides an introduction to understanding how emerging media technologies like social media, smartphones, algorithms, and artificial intelligence affect our world. Specifically, we will explore how our experiences with technology affect our psychological well-being and mental health, and the informational well-being of our communities.

Focuses on understanding the challenges innovators and adopters of technologies face at various stages of technology maturity, the tools and strategic choices organizations have at their disposal, exemplified across a range of technologies.

Introduction to circuit modeling and analysis. Topics include creating the models of typical components in electronic circuits and simplifying non-linear models for restricted ranges of operation (small signal model); and using network theory to solve linear and non-linear circuits under static and dynamic operations.

Representation, analysis, techniques, and principles for manipulation of basic combinatoric structures used in computer science. Rigorous reasoning is emphasized.

CS106A introduces code and computer programming for people who have not programmed before. Code and programming are central to so much in modern life, yet code can appear to be impossibly opaque. By working gradually and with its army of section leaders, CS106A takes students into the world code, building things they could not have imagined.

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets), and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent.

Introduction to key concepts from calculus (differentiation and integration), probability (discrete and continuous random variables), and linear algebra (vector spaces, matrices, and linear systems).

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality-constrained problems; interior-point methods for inequality-constrained problems. Applications to signal processing, communications, control, analog, and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering.

Quantitative evaluation of technologies and techniques for reducing energy demand of residential-scale buildings. Heating and cooling load calculations, financial analysis, passive-solar design techniques, water heating systems, photovoltaic system sizing for net-zero-energy all-electric homes.

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor.

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries.

Client-side technologies are used to create websites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax. Prerequisite: programming experience at the level of CS106A.

A hands-on introduction to the methods of principles of data science. Strategies for analyzing and visualizing tabular data, including common patterns and pitfalls. Data acquisition through web scraping and REST APIs. Core principles of machine learning: supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. Introduction to data of different shapes and sizes, including text, image, and geospatial data.

This course offers an examination of virtual reality (VR) and digital human representations, focusing on creation, interaction, and applications. Students will explore the multidisciplinary landscape of VR including psychology, computer science, and communication.