Lecture

Machine Learning for Behavioral Data

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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

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Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

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Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

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Addresses data collection, annotation processes, and biases in natural language processing.

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Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Model Evaluation

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

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

Building Educational Chatbots: Project Overview

Outlines the project for building educational chatbots for EPFL courses, detailing its structure and requirements.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

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Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Active Learning for Molecular Design

Covers machine learning approaches for material design, practical examples, and software tools for research.

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Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

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

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