Machine Learning: Inferring Information From Data

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Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Mathematics of Data: Models and Learning

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

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Biases, ML performance and adversarial ML threats

Explores Machine Learning basics, adversarial conditions, privacy implications, and deployment challenges, highlighting biases and adversarial threats.

Supervised Learning in Asset Pricing

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Knowledge Inference for Graphs

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

Financial Time Series Analysis

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Machine Learning Biases

Explores machine learning basics, adversarial challenges, biases, distributional shift, and deployment complexities.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.