Agentic Machine Learning

Learn all the central concepts in machine learning and how to use AI coding agents to explore data, build machine learning models, validate performance, and accelerate domain-specific analysis. Course Facts A practical course for professionals who want to learn, evaluate, and apply modern machine learning in realistic domain contexts. DATES 9th to 13th of Nov., 2026 TIME 9.00 to 16.00 each day LOCATION Technical University of Denmark, Kgs. Lyngby PRICE 20.000 DKK excl. moms (Catering included) INCLUDED Guidance and insights from ML and agentic coding experts (New book) Agentic Machine Learning Access to state-of-the-art agentic tools The course is based on our highly successful machine learning course at DTU, which is attended by approximately a thousand students each year, and has been adapted to a professional audience with a focus on practical applications and AI-assisted workflows. The teachers are researchers with many years of experience teaching machine learning. Participants will also get access to our new book on Agentic Machine Learning. .course-facts { max-width: 1320px; margin: 0 auto; padding: 50px 20px; font-family: Arial, Helvetica, sans-serif; color: #111; } .course-header { display: grid; grid-template-columns: 1fr 1.1fr; gap: 60px; align-items: start; margin-bottom: 50px; } .course-header h2 { font-size: 56px; line-height: 1; margin: 0; font-weight: 800; } .course-header p { font-size: 20px; line-height: 1.45; color: #5f6770; margin: 0; } .facts-grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 16px; margin-bottom: 16px; } .fact-box, .included-box { border: 1px solid #ddd6cf; background: #fff; padding: 28px 24px; } .fact-box span, .included-box span { display: block; color: #b00000; font-size: 14px; font-weight: 800; letter-spacing: 1px; margin-bottom: 24px; } .fact-box strong { display: block; font-size: 22px; line-height: 1.25; font-weight: 800; } .fact-box p { margin: 6px 0 0; font-size: 18px; } .included-box { margin-bottom: 32px; } .included-tags { display: flex; flex-wrap: wrap; gap: 12px; } .included-tags strong { background: #f2eee9; border: 1px solid #ddd6cf; padding: 14px 16px; font-size: 18px; font-weight: 800; } .course-text { max-width: 980px; font-size: 20px; line-height: 1.5; color: #333; } .course-text p { margin: 0 0 22px; } @media (max-width: 900px) { .course-header, .facts-grid { grid-template-columns: 1fr; } .course-header h2 { font-size: 42px; } .course-header { gap: 20px; } } After the Course Participants leave with a grounded way to judge when machine learning is useful, how to apply it responsibly, and how AI agents can support the analytical workflow. FOUNDATION A solid foundation in machine learning based on state-of-the-art research. JUDGEMENT The ability to assess whether ML is suitable for a domain-specific problem. WORKFLOW Prepare and evaluate agentic machine learning workflows. RISK Identify and mitigate known issues when applying machine learning. EXECUTION Design and execute analysis through AI agents. Who It Is For Designed for professionals with existing domain knowledge who want to approach agentic machine learning and data science with confidence and realism. No prior experience with machine learning or agentic AI is required. Participants should have knowledge of basic statistics and linear algebra. Key mathematical concepts will be introduced as needed. While programming will be done through AI agents, participants should be comfortable reading and evaluating code snippets in Python. Participants are encouraged to bring example datasets or use cases from their own domain, alongside the course-provided datasets. Bring your own laptop The course includes hands-on analysis, model-building, and AI-agent-assisted workflows, so participants should bring a laptop for the practical sessions. .course-section { width: 100%; border-top: 1px solid #ddd6cf; padding: 70px 20px; font-family: Arial, Helvetica, sans-serif; color: #111; box-sizing: border-box; } .course-section * { box-sizing: border-box; } .course-wrap { width: 100%; max-width: 1080px; margin: 0 auto; } .course-header { display: grid; grid-template-columns: minmax(260px, 0.9fr) minmax(280px, 1.1fr); gap: 50px; align-items: start; margin-bottom: 42px; } .course-header h2 { margin: 0; font-size: clamp(42px, 5vw, 58px); font-weight: 800; line-height: 1; letter-spacing: -1px; } .course-header p { margin: 0; max-width: 620px; font-size: 18px; line-height: 1.45; color: #5f6770; } .course-card-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 16px; } .course-card { min-height: 210px; border: 1px solid #ddd6cf; background: #fff; padding: 26px 24px; } .course-card span { display: block; margin-bottom: 22px; color: #b00000; font-size: 13px; font-weight: 800; letter-spacing: 1px; } .course-card strong { display: block; font-size: 18px; line-height: 1.28; font-weight: 800; } .who-grid { display: grid; grid-template-columns: minmax(0, 1.6fr) minmax(260px, 0.8fr); gap: 50px; align-items: start; } .who-text { max-width: 680px; } .who-text p { margin: 0 0 22px; font-size: 18px; line-height: 1.5; color: #333; } .who-text a { color: #333; text-decoration: underline; } .who-text a:hover { color: #b00000; } .note-box { background: #f2eee9; border: 1px solid #ddd6cf; padding: 28px 26px; } .note-box strong { display: block; margin-bottom: 16px; font-size: 18px; font-weight: 800; } .note-box p { margin: 0; font-size: 17px; line-height: 1.5; color: #333; } @media (max-width: 900px) { .course-header, .who-grid { grid-template-columns: 1fr; gap: 24px; } .course-card-grid { grid-template-columns: repeat(2, 1fr); } } @media (max-width: 560px) { .course-section { padding: 50px 18px; } .course-card-grid { grid-template-columns: 1fr; } .course-card { min-height: auto; } } Course Team The instructors have years of experience in teaching, researching, and applying machine learning. This knowledge has been distilled into this course and the accompanying book. Jesper Hinrich A postdoctoral researcher at DTU Compute focusing on statistical machine learning, tensor modelling, and data analysis for complex scientific data. He has extensive teaching experience across machine learning, statistics, software engineering, and applied data science, and brings extensive experience with state-of-the-art AI coding agents for accelerating workflows. Morten Mørup Professor of machine learning for the life sciences at DTU Compute with extensive teaching experience from introductory to advanced machine learning courses. He has been course responsible for many years for DTU Compute’s successful introductory machine learning course as well as served as head of studies for the AI and Data B.Sc. education. About DTU Compute The Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark, bringing together research and teaching across mathematics, statistics, computer science, and data-driven technology. Visit DTU Compute .course-team { width: 100%; margin: 0; padding: 40px 0; font-family: inherit; } /* Header */ .team-header { display: grid; grid-template-columns: minmax(180px,0.8fr) minmax(280px,1.2fr); gap: 40px; margin-bottom: 32px; } .team-header h2 { margin: 0; font-size: clamp(42px,5vw,64px); line-height: .95; font-weight: 800; } .team-header p { margin: 0; font-size: 18px; line-height: 1.45; } /* Grid */ .team-grid { display: grid; grid-template-columns: 1fr; gap: 22px; } @media (min-width:900px){ .team-grid{ grid-template-columns:1fr 1fr; } .compute-box{ grid-column:1/-1; } } /* Cards */ .team-card{ border:1px solid #ddd6cf; background:#fff; padding:22px; overflow:hidden; } .team-card img{ float:left; width:130px; height:130px; object-fit:cover; margin:0 24px 14px 0; } .team-card h3{ margin:0 0 12px; font-size:24px; line-height:1.15; font-weight:800; } .team-card p{ margin:0; font-size:17px; line-height:1.55; } /* Compute box */ .compute-box{ background:#eee8e1; border:1px solid #d8d0c8; padding:28px; } .compute-box h3{ margin:0 0 12px; font-size:24px; line-height:1.15; font-weight:800; } .compute-box p{ margin:0; font-size:17px; line-height:1.55; } .compute-box a{ display:inline-block; margin-top:24px; color:#b00000; text-decoration:none; font-size:17px; font-weight:800; } .compute-box a:hover{ text-decoration:underline; } /* Responsive */ @media (max-width:700px){ .team-header{ grid-template-columns:1fr; gap:18px; } .team-card img{ width:100px; height:100px; margin:0 18px 12px 0; } } @media (max-width:480px){ .team-card img{ float:none; display:block; margin:0 0 18px; width:100px; height:100px; } } Five-Day Programme The week moves from introductory machine learning and agentic coding to core machine learning concepts considering supervised and unsupervised learning, ending with advanced agentic machine learning and future perspectives. Monday Introduction MORNING Introduction to machine learning and agentic coding AFTERNOON Data preparation and data quality assessment Tuesday Supervised learning MORNING Statistical learning, simple explainable models, and measures of performance AFTERNOON Cross-validation, generalization, and statistical performance assessment Wednesday Supervised learning MORNING Bias-variance trade-off, regularization, and ensembling AFTERNOON Deep learning and over-parameterization Thursday Unsupervised learning MORNING Representation learning AFTERNOON Clustering Friday Unsupervised learning MORNING Density estimation and outlier detection AFTERNOON Advanced agentic machine learning .programme-section { width: 100%; border-top: 1px solid #ddd6cf; padding: 70px 20px; font-family: Arial, Helvetica, sans-serif; color: #111; box-sizing: border-box; overflow: hidden; } .programme-section * { box-sizing: border-box; } .programme-wrap { width: 100%; max-width: 1080px; margin: 0 auto; } .programme-header { display: grid; grid-template-columns: minmax(0, 1fr) minmax(320px, 1fr); gap: 70px; align-items: start; margin-bottom: 42px; } .programme-header h2 { margin: 0; max-width: 420px; font-size: clamp(42px, 5vw, 58px); font-weight: 800; line-height: 1; letter-spacing: -1px; overflow-wrap: break-word; word-break: normal; } .programme-header p { margin: 0; max-width: 560px; font-size: 18px; line-height: 1.45; color: #5f6770; } .programme-list { display: grid; gap: 14px; } .programme-row { display: grid; grid-template-columns: 250px 1fr 1fr; border: 1px solid #ddd6cf; background: #fff; } .programme-day, .programme-slot { padding: 24px 22px; } .programme-day, .programme-slot:first-of-type { border-right: 1px solid #ddd6cf; } .programme-day strong, .programme-day span { display: block; color: #b00000; font-size: 18px; line-height: 1.35; font-weight: 800; } .programme-slot span { display: block; margin-bottom: 14px; color: #666; font-size: 13px; font-weight: 800; letter-spacing: 1px; } .programme-slot strong { display: block; font-size: 18px; line-height: 1.35; font-weight: 800; } @media (max-width: 900px) { .programme-header { grid-template-columns: 1fr; gap: 24px; } .programme-header h2, .programme-header p { max-width: none; } .programme-row { grid-template-columns: 1fr; } .programme-day, .programme-slot:first-of-type { border-right: none; border-bottom: 1px solid #ddd6cf; } } @media (max-width: 560px) { .programme-section { padding: 50px 18px; } .programme-header h2 { font-size: clamp(38px, 13vw, 52px); } .programme-day, .programme-slot { padding: 22px 20px; } }