FS261662 Advanced Machine Learning for Data Analysis

Semester

FS26

Description

Advanced machine learning techniques such as large language models (LLMs), vision-language models (VLMs) as well as dimensionality reduction techniques such as t-SNE or UMAP have become foundational tools in data analysis and artificial intelligence. They are increasingly important in healthcare, research, and industry applications. In this course, students will learn to:

  • Use state-of-the-art LLMs through common interfaces such as Hugging Face, Google Gemini, and OpenAI.
  • Run transformer-based models locally on their laptops and understand practical hardware considerations.
  • Apply LLMs to real-world healthcare tasks such as processing large text corpora, screening abstracts, classification, and summarization.
  • Work with different model families, including BERT-like models, GPT-style models, and CLIP for multimodal tasks.
  • Design effective prompts and recognize common failure modes, biases, and ethical challenges of LLMs.
  • Build complete Python workflows that integrate LLMs into data analysis and machine-learning pipelines.

The course combines lectures with extensive hands-on exercises, enabling students to directly apply LLM-based methods to relevant healthcare examples.

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