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Related lectures (32)

Page 2 of 4

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

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

Knowledge Representation: Semantics and Data Structures

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Data Representation: Models and Systems

Discusses data representation through models and systems, covering mathematical models, data structures, levels of modeling, and managing data.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Stockage et transmission des données 1: introduction; besoin de structure

Discusses the importance of structuring data for efficient storage and retrieval.

Introduction to Machine Learning

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

Atomistic Machine Learning: Physics and Data

Explores Atomistic Machine Learning, integrating physical principles into models to predict molecular properties accurately.

Introduction to R Programming for Genetics & Genomics

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.