How to Speak

This course focuses on the design and implementation of secure computer systems, exploring various threat models and security techniques. Learners will engage with topics such as operating system security, network protocols, and hardware security, drawing insights from recent research papers.

This course explores the fundamental role of data structures in computer science, emphasizing their importance in developing efficient algorithms. Learners will study advanced topics, including dynamic graphs, memory hierarchy, and hashing, while engaging with current research directions in the field.

This course delves into the intricacies of high-level programming language implementation, focusing on compiler design and optimization techniques. Learners will engage in a hands-on project that involves building a compiler in Java® that generates MIPS executable machine code.

This course provides a comprehensive introduction to systems engineering, focusing on both classical and modern approaches. Learners will explore key concepts such as stakeholder analysis, requirements definition, system architecture, and design optimization, applying these principles to real-world projects.

This course explores the intricacies of poker theory and the application of poker analytics in investment management and trading. Learners will delve into decision-making frameworks and strategies that can enhance their understanding of both poker and financial environments.

This course explores the practical aspects of proving that certain problems cannot be solved efficiently, focusing on reductions and techniques for demonstrating computational hardness across various complexity classes. Learners will engage with interesting problems and develop a deeper understanding of algorithmic limitations.

This course explores the evolution of proofs in computer science, focusing on interactive proofs, multi-prover interactive proofs, and probabilistically checkable proofs. Learners will discover how to leverage cryptography to transform these advanced proof systems into computationally sound non-interactive arguments (SNARGs).

This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.

This course presents an innovative approach to teaching linear algebra, focusing on key concepts and their interconnections. Learners will explore foundational topics and gain insights into the structure and applications of linear algebra through engaging video content.

This course introduces the mathematical modeling of computational problems and the common algorithms, algorithmic paradigms, and data structures used to solve them. It emphasizes the relationship between algorithms and programming, and introduces basic performance measurement and analysis techniques.

This course covers elementary discrete mathematics for science and engineering, focusing on mathematical tools and proof techniques useful in computer science. Learners will explore topics such as logical notation, sets, relations, graph theory, state machines, and algorithms.

This course provides a fast-paced introduction to deep learning, focusing on practical model-building for complex problems involving unstructured data. Learners will explore deep neural networks, convolutional networks, transformers, and generative models like ChatGPT and Midjourney.

This course covers classical and modern developments in graph theory and additive combinatorics, with a focus on topics and themes that connect the two subjects. It also includes an introduction to current research topics and open problems.

This course delves into the essential concepts of matrix calculus, focusing on its applications in machine learning and large-scale optimization. Learners will explore techniques to understand and compute derivatives of matrices and their factorizations, enhancing their ability to tackle complex mathematical operations in modern computing.

This course focuses on computability and computational complexity theory. Learners will cover topics such as regular languages, context-free languages, decidable and undecidable problems, and recursive function theory.

This course covers fundamental discrete mathematics for computer science and engineering. It focuses on mathematical definitions, proofs, and applicable methods, covering various topics such as logic symbols, proof techniques, sets, relations, and graph theory.

This course provides practical instruction in the design and analysis of non-digital games. Students cover the texts, tools, references and historical context to analyze and compare game designs across a variety of genres, including sports, game shows, games of chance, card games, schoolyard games, board games, and role–playing games.

This course is a graduate-level introduction to the probabilistic methods, a fundamental and powerful technique in combinatorics and theoretical computer science. Learners will explore how to demonstrate the existence of combinatorial objects and validate random constructions with positive probability.

This class provides some answers to basic questions about the nature of human language. Throughout the course, learners will examine how human language functions as a complex yet law-governed mental system, exploring topics such as language acquisition, historical language changes, and language endangerment.

This course covers the process of identifying and quantifying market opportunities, then conceptualizing, planning, and starting a new, technology-based enterprise. Students develop detailed business plans for a startup.

This course explores the intersection of art and social responsibility, focusing on how intentional disruptions can challenge systems of oppression. Learners will engage with critical issues such as access to resources and the global water crisis through creative practices and pedagogical strategies.

This course introduces the Reflective Practice approach developed by Donald Schön, focusing on how professionals can effectively combine their knowledge with practical situations. Learners will explore theories of learning and innovation to enhance their cognitive resources and improve their professional practice.