Teaching Assistant for Stochastics and Machine Learning (252-0870-00L) at ETH Zurich during Spring 2025 semester.

Course Overview

This undergraduate-level course covers fundamental concepts in probability theory and machine learning, bridging statistical foundations with modern ML techniques.

Part I: Stochastics

• Probability spaces, probability measures, independence
• Conditional probabilities, Bayes’ theorem
• Random variables, distributions, expected value, variance
• Random vectors, multivariate distributions
• Law of large numbers, central limit theorem
• Descriptive statistics, parameter estimation, statistical tests

Part II: Machine Learning

• Linear and logistic regression
• Regularization and bias-variance tradeoff
• Ensemble methods and unsupervised learning
• Deep learning: neural networks, CNNs, transformers
• Generative models: autoencoders, GANs
• Reinforcement learning: Markov decision processes, Q-learning

Teaching Responsibilities

• Conducted exercise sessions and tutorial groups
• Assisted students with theoretical concepts and practical implementations
• Graded assignments and provided detailed feedback
• Supported course administration and student inquiries

Course Details

• Course Code: 252-0870-00L
• Instructors: Carlos Cotrini Jimenez, Patrick Cheridito
• Semester: Spring 2025
• Level: Undergraduate