Certificates for Artificial Intelligence

MLx Fundamentals 25

This program offered a comprehensive 18-hour course that included theory and practical sessions covering key areas in modern machine learning, such as Introduction to ML, Deep Learning & Representation Learning, Optimisation, and Generative AI (vision & NLP).

MLx Generative AI 25

MLx Health & Bio 25

This program offered a comprehensive 23-hour course that included overview of machine learning theory in Representation Learning, Geometrical Deep Learning, Large Language Models, and Computer Vision, with in-depth applications in health and biomedical domains, including drug discovery, genomics, electronic health records, medical imaging, and clinical NLP

MLx Representation Learning & Generative AI 25

MLx Representation Learning & Generative AI 25 is currently ongoing! We aim to have the recordings uploaded here around a couple days after the lectures are finished.

Statistical Machine Learning

Master the mathematical foundations and statistical methods underlying modern machine learning. This certification covers probability theory, Bayesian modeling, optimization, and advanced statistical inference techniques essential for rigorous ML research and development.

Representation Learning

Explore cutting-edge techniques for learning powerful data representations. This certification covers self-supervised learning, transformers, multimodal models, and generative AI, preparing you for advanced roles in modern deep learning and foundation model development.

Evals for AI Products — from model.fit() to market.fit()

This learning plan covers the full landscape of evaluation for AI systems, moving from classical machine-learning diagnostics to the challenges of modern generative, agentic, multimodal, and enterprise-scale deployments. Learners progress through foundational metrics, LLM and agent evaluation, multimodal testing, domain-specific benchmarks, operational and safety assessments, and commercial ROI evaluation. By the end, participants can design rigorous eval suites that reflect scientific best practices, real-world constraints, and business outcomes. The course blends technical depth with practical workflows that are essential for trustworthy, high-impact AI products.

MLx Health & Bio 24

This program offered a comprehensive 19-hour course that included theory and practical sessions covering key areas in modern machine learning, such as AI for Healthcare, Longitudinal Language Processing from User Generated Content, Recent Trends in Computer Vision, ML and Medical Imaging, ML for Wearables in Medicine, ML for Drug Discovery, and AI Safety.

MLx Fundamentals 24

This program offered a comprehensive 11-hour course that included theory and practical sessions covering key areas in modern machine learning, such as Factorisation methods in ML, Causal Representation Learning and Related Machine Learning Tasks, Deep Learning, Generative AI (vision and NLP), Optimisation (including DNN), and an Introduction to Statistical Machine Learning.

MLx Representation Learning & Generative AI 24

This program offered a comprehensive course covering 22 hours of advanced lectures and practical sessions on machine learning theory in Representation Learning and Generative AI, including Large Language Models and Agents, deep learning techniques, Neural and Behavioural Comparisons Between Humans and Machines, Uncertainty Quantification, Deep Learning in Financial Markets, and Representation Learning and Generative AI in Vision.

MLx Generative AI 24

This program offered a comprehensive 21-hour lectures on advanced theoretical topics in representation learning, agentic AI, human + AI alignment, building GenAI products, foundation models, and large frontier models in applied domains.

MLx Fundamentals 23

This program offered a comprehensive 5-hour course that included theory and practical sessions covering key areas in modern machine learning, such as Optimisation for Machine Learning, Fundamentals (Mathematics for Machine Learning, Reinforcement Learning, Bayesian Optimisations), and Understanding the Basic Pillars Behind GPT4.

MLx Health & Bio 23

This program offered a comprehensive 18-hour course that included theory and practical sessions covering key areas in modern machine learning, such ML in Medical Imaging, Computer Vision (including past and current implementations), Machine Learning for Electronic Health Records, Graph Representation Learning and Diffusion, and Fitting a Model into the Market.

MLx Finance 23

This program offered a comprehensive 19-hour course that included theory and practical sessions covering key areas in modern machine learning, such as Human-Centered NLP for Positive Imact, Factor Selection and Aggregation in Asset Pricing, Integrating Factual & Algorithmic Knowledge in Neural Networks, Graph Clustering & Ranking for Lead-lag Detection in Equity Markets, Deep Learning in Finance, Reinforcement Learning in Finance and Automated Trading, and Hybrid Recurrent Architecture for Quantum-Classical NLP.

MLx Fundamentals 22

This program offered a comprehensive 5-hour course that included theory and practical sessions covering key areas in ML fundamentals, advanced topics in ML theory.

MLx Health 22

This program offered a comprehensive 12-hour course that included theory and practical sessions covering key areas on ML in medicine.

MLx Finance 22

This program offered a comprehensive 12-hour course that included theory and practical sessions covering key areas on ML in Finance.

OxML 2021

OxML 2021 offered a comprehensive course, covering 70+ hours of lectures including 12 hours of ML fundamentals and 58 hours of advanced topics in ML theory and its application in various areas of Sustainable Development Goals.

MLx Fundamentals 21

This program offered a comprehensive 5-hour course that included theory and practical sessions covering key areas in ML fundamentals, advanced topics in ML theory.

OxML 2020

OxML 2020 offered a comprehensive course, covering 60+ hours of lectures including 12 hours of ML fundamentals and 48 hours of advanced topics in ML theory.