Matrix Factorization: Information Extraction

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Matrix Factorization: Linking Text to Knowledge Bases

Explores distant supervision for linking text to knowledge bases using entity extraction and classifiers.

Embedding Models: Concepts and Retrieval

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

Mathematics of Data: Models and Learning

Explores models, learning paradigms, and applications in Mathematics of Data.

Understanding Chaos in Quantum Field Theories

Explores chaos in quantum field theories, focusing on conformal symmetry, OPE coefficients, and random matrix universality.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

Knowledge Inference for Graphs

Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.