CourseAI

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 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 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 examines the choices and constraints regarding sources and uses of energy by households, firms, and governments through a number of frameworks to describe and explain behavior at various levels of aggregation. Examples include a wide range of countries, scope, settings, and analytical approaches.

This course covers key topics in discrete applied mathematics, focusing on counting, generating functions, probability, and more. Learners will engage with both mathematical concepts and effective communication through writing assignments.

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 builds on the foundations of programming in Python, emphasizing computational thinking and data science principles. Learners will explore how computation can effectively solve real-world problems, empowering them to write functional programs for various applications.

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 is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

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 explores the mathematical concepts and techniques essential for the financial industry, blending theoretical mathematics with practical applications. Learners will study topics such as linear algebra, probability, statistics, and stochastic processes, guided by both MIT mathematicians and industry professionals.

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 emphasizes dynamic models of growth and development, exploring topics such as migration, modernization, and technological change. Learners will study the interplay between economic theory and real-world applications in developing countries, focusing on income distribution, institutional change, and the functioning of financial markets.

This course explores the fundamental principles of artificial intelligence, focusing on both theoretical concepts and practical applications. Learners will study algorithms, machine learning techniques, and the ethical implications of AI technologies.

This course equips learners with the essential knowledge and skills for teaching science and engineering at the college level. Participants will explore innovative teaching methods, engage in active learning, and develop their own teaching practices through research and presentations.

This course provides an introduction to chemical oceanography, focusing on the composition and dynamics of seawater and the biogeochemical cycles that shape oceanic environments. Learners will explore key concepts such as reservoir models, nutrient cycling, and the impact of human activities on ocean chemistry.

This course explores the integration of architectural design within natural landscapes, focusing on the relationship between built environments and their ecological contexts. Learners will develop skills in analyzing landscape and climate to inform their design decisions through drawings and models.

This course focuses on developing innovative information systems tailored for resource-poor settings, aiming to enhance health outcomes and strengthen organizational infrastructures. Learners will explore the intersection of public health, technology, and collaborative ecosystems to address healthcare challenges in developing countries.

This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality.

This course explores the fundamental principles of artificial intelligence, focusing on both theoretical concepts and practical applications. Learners will study algorithms, machine learning techniques, and the ethical implications of AI technologies.

This course delves into the foundational concepts of artificial intelligence, exploring its principles, applications, and ethical implications. Learners will study key algorithms, machine learning techniques, and the societal impacts of AI technologies.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

This is an introductory lecture designed to help you understand the core concepts of non-invasive EEG-based Brain-Machine Interfaces (BMI) and the process of Motor Imagery-based signal processing. It provides an easy-to-follow explanation of the entire workflow, from the basic principles of EEG signals to Motor Imagery experiments, data processing, and how actual BMI systems operate.

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

Learn the core principles and practical implementation of Classic RAG, Graph RAG, and Agentic RAG. You will design dynamic routing systems that optimize token efficiency and latency, and build long-term memory systems that combine GraphRAG's relational network reasoning with Agentic RAG's self-evaluation loops. This course is designed to complete your expertise in designing advanced RAG architectures that can be immediately applied in professional practice.

Find your way through the sea of data and explore the future of artificial intelligence. This course explores the two massive technological trends of Big Data and Artificial Intelligence (AI), examining how they are bringing innovation to our lives and work.

Understand the operating principles of generative AI models using deep learning and acquire practical application skills through hands-on exercises.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.

The Master Guide to AI for Java Developers! This course shatters the prejudice that AI is the exclusive domain of Python and empowers your Java applications with AI-driven eyes, ears, voice, and even coding capabilities. Grow into an AI Architect based on the OpenAI API. You will directly develop various AI experts such as an AI writer, translator, artist, and coding companion. You will also build a multi-functional AI assistant that integrates core features including text summarization, sentiment analysis, image description, Java code generation, speech-to-text/text-to-speech conversion, and image/subtitle generation. This is a golden opportunity to maximize the competitiveness of existing Java systems by learning how to assign AI roles through system messages and even implement harmful content moderation.

Create high-quality AI videos with Midjourney & Runway. Director Dong-ah Kim, who collaborated with Samsung, will personally guide you through the step-by-step process of AI video production based on a virtual brand.

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

Learn machine learning techniques using Python and improve your ability to extract information from real-world data and develop predictive models!

This course introduces quantitative approaches to understanding brain and cognitive functions. Learners will explore mathematical descriptions of neurons, their responses to stimuli, and foundational data analysis tools in neuroscience.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will gain a broad range of knowledge, from establishing AI-based system testing strategies to designing and executing test cases, as well as utilizing AI for test support. This course is based on the ISTQB AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.