Lecture

Secure Collaborative Systems: Building Efficient Protocols for Linear Models

Related lectures (31)

Introduces Secure Multiparty Computation techniques, covering theoretical frameworks, security models, and practical applications in privacy-preserving cryptography.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

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.

Safe Machine Learning: Cryptographic Opportunities

Explores the intersection between machine learning and cryptography, focusing on safe machine learning through cryptographic tools and models.

Introduction to Cryptography: Fundamentals and Applications

Covers the fundamentals of cryptography, including symmetric encryption, digital signatures, and secure communication standards.

Introduction to Applied Data Analysis

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

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Efficient Post-Quantum Commutative Group Action

Explores the CSIDH algorithm, a post-quantum cryptographic scheme based on isogenies, discussing its history, inner workings, and security.

Machine Learning Fundamentals: Regularization and Cross-validation

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Introduction to R Programming for Genetics & Genomics

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

Regression Diagnostics

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Privacy Preserving Crypto I: Secure Multi-Party Computation

Explores Secure Multi-Party Computation, cryptographic techniques, threat models, additive secret shares, and real-world applications of privacy-preserving protocols.

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Provable Benefits of Overparameterization in Model Compression

Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Generator of a Group

Explores checking a generator of a group, the Diffie-Hellman key exchange, and cryptographic algorithms.

Assessing Significance and Fit

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Secure Communications: Biometric Passport Case Study

Explores the shift to secure communications through the biometric passport case study, covering RFID, identity examples, MRTD, EAC, and cryptographic protocols.