Introduction to Biological Engineering Design

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 focuses on learning several rules to improve speaking skills in important situations. Learners can master effective presentation methods for various contexts, such as interviews, thesis defenses, and oral exams.

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 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 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 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 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 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 explores how financial technology, or FinTech, is transforming the financial services industry through innovative technologies. Learners will study key trends such as AI, blockchain, and open APIs, gaining insights into the evolving market landscape and regulatory environment.

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 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 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 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 emphasizes the structural principles of digital systems and introduces architectures commonly applied across various technologies. Learners will cover a wide range of topics, including multi-level implementation strategies, the definition and mechanisms of new primitive elements, and performance measurement.

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 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 is an intermediate algorithm course that focuses on efficient algorithm design and analysis techniques. Learners will master various algorithm design paradigms, such as divide and conquer, dynamic programming, and greedy algorithms.

This course delves into the critical issues of bias and fairness in machine learning, focusing on their implications for international development. Learners will explore strategies for the appropriate use of ML to promote equity and social justice.

This course focuses on developing literacy skills in Chinese for learners with basic conversational abilities. Students will engage in reading and writing in both full and simplified characters, enhancing their everyday language usage.

This course introduces the mathematical modeling of computational problems. It covers common algorithms, algorithmic paradigms, and the data structures used to solve these problems.

This course covers numerical methods essential for solving complex problems in chemical engineering, including heat and mass transfer, fluid mechanics, and molecular simulation. Learners will explore various computational techniques and their applications in real-world engineering scenarios.

This course provides a foundation for modern research in atomic and optical physics, covering non-classical states of light, multi-photon processes, atomic interactions, and experimental methods.

This is a foundational chemistry course for students with a strong background in chemistry. It focuses on the basic principles of the electronic structure of atoms and molecules, thermodynamics, acid-base and redox equilibria, chemical kinetics, and catalysis.

This course covers quantum physics including applications of modern physics, and explores topics such as the general formalism of quantum mechanics, the harmonic oscillator, three-dimensional quantum mechanics, angular momentum, spin, and the addition of angular momentum.