Subtopic
Machine Learning
12 lessons to explore at your own pace.
Lessons in Machine Learning
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Self-paced lesson · ~18 min read
Machine Learning for Finance: Validation, Neural Pricing, and Reinforcement-Learning Hedges
Machine learning fits patterns from data to support predictions or decisions. In finance, a model can estimate a return, approximate an expensive pricing engine, organize risk exposures,…
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Self-paced lesson · ~16 min read
Training Deep Neural Networks: Keras, TensorFlow, and Tabular Data
Training a neural network means turning a mathematical prediction rule into an experiment: prepare the data, choose an objective, update parameters, and measure performance on unseen…
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Self-paced lesson · ~17 min read
Deep Learning for Computer Vision: Building Convolutional Neural Networks from Scratch
Images contain structure: neighboring pixels form edges, edges combine into shapes, and shapes help identify objects. A convolutional neural network, or CNN, builds that structure into…
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Self-paced lesson · ~16 min read
Deep Learning for Computer Vision: Transfer Learning, Fine-Tuning, and Hugging Face
Training every visual feature from random initialization can be wasteful when you have only a few hundred labeled images. Transfer learning starts from representations learned on another…
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Self-paced lesson · ~16 min read
Deep Learning for Natural Language: The Basics
Text carries explanations, opinions, stories, instructions, and conversations. Natural language processing, or NLP, turns this unstructured material into representations that…
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Self-paced lesson · ~17 min read
Deep Learning for Natural Language: Word Embeddings
A word ID tells a model which token it has encountered, but says nothing about what that token means. Word embeddings replace isolated token indicators with learned dense vectors. This…
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Self-paced lesson · ~15 min read
Deep Learning for Natural Language: Transformers
A static embedding gives a token one vector. A Transformer combines token representations with information about their positions and surrounding tokens, producing a contextual state at…
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Self-paced lesson · ~14 min read
Deep Learning for Natural Language: Transformers and Self-Supervised Learning
A Transformer specifies how token representations interact. A learning objective specifies what those interactions should help predict. Self-supervised learning connects the two by…
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Self-paced lesson · ~15 min read
Generative AI: Large Language Models and Retrieval-Augmented Generation
A language model can produce fluent answers from patterns learned during training, but an application often needs information that is private, recent, or too extensive to fit in one…
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Self-paced lesson · ~15 min read
Generative AI: Adapting LLMs with Parameter-Efficient Fine-Tuning
Adapting a large language model can mean changing its instructions, supplying external evidence, or training parameters for a target task. Parameter-efficient fine-tuning, or PEFT, makes…
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Self-paced lesson · ~16 min read
Principal Component Analysis in Finance: Yield-Curve Factors, Hedging, and Relative Value
Hundreds of financial series can share a few major sources of variation. Principal component analysis (PCA) finds directions that summarize that variation. In rates, those directions…
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Self-paced lesson · ~18 min read
Applying Data Science and Artificial Intelligence to Managing Biomedical Portfolios
A promising biological discovery is not yet a medicine, and a promising medicine is not yet a financeable investment. Biomedical portfolio management connects scientific evidence,…