Science des données
PHYS-231
Media
PHYS-231 Science des données
Lecture 14 -Part2 - Phase transitions (2024)
17.12.2024, 11:13
Lecture 14 -Part1 - Phase transitions (2024)
17.12.2024, 11:11
Lecture 04 -Part2 - Linear Regression Continued (2024)
01.10.2024, 11:00
Lecture 04 -Part1 - Linear Regression Continued (2024)
01.10.2024, 10:58
Lecture 14 -Part2 - Phase transitions
19.12.2023, 10:36
Lecture 14 -Part1 - Phase transitions
19.12.2023, 09:07
Lecture 13 -Part2 - Theory and examples of MCMC
12.12.2023, 10:07
Lecture 13 -Part1 - Theory and examples of MCMC
12.12.2023, 09:05
Lecture 12 -Part2 - Sampling with Monte Carlo Markov Chains
05.12.2023, 10:05
Lecture 12 -Part1 - Sampling with Monte Carlo Markov Chains
05.12.2023, 09:02
Lecture 11 -Part2 - Brownian motion, diffusion, random walks and maximum entropy principle
28.11.2023, 10:06
Lecture 11 -Part1 - Brownian motion, diffusion, random walks and maximum entropy principle
28.11.2023, 09:06
Lecture 10 -Part2 - Back to linear regression, estimation of the errors
21.11.2023, 15:57
Lecture 10 -Part1 - Back to linear regression, estimation of the errors
21.11.2023, 15:56
Lecture 09 -Part2 - Maximum Likelihood, examples and theory
14.11.2023, 10:35
Lecture 09 -Part1 - Maximum likelihood, examples and theory
14.11.2023, 10:35
Lecture 08 -Part2 - Central limit theorem, statistics and maximum likelihood
07.11.2023, 10:14
Lecture 08 -Part1 - Central limit theorem, statistics and maximum likelihood
07.11.2023, 10:12
Lecture 07 -Part2- Propagation of uncertainty II
31.10.2023, 15:26
Lecture 07 -Part1- Propagation of uncertainty II
31.10.2023, 15:23
Lecture 06 -Part2- Generation of random variables and propagation of uncertainty I
24.10.2023, 14:24
Lecture 06 -Part1- Generation of random variables and propagation of uncertainty I
24.10.2023, 14:22
Lecture 05 -Part2- Gradient descent
17.10.2023, 10:54
Lecture 05 -Part1- Gradient descent
17.10.2023, 10:37
Lecture 04 -Part2- Linear regression continued
10.10.2023, 10:55
Linear regression and gradient descent
Lecture 03 -Part2- Linear regression and least squares method
05.10.2023, 13:10
Lecture 03 -Part1- Linear regression and least squares method
05.10.2023, 13:07
Lecture 02 -Part2- Principal component analysis
27.09.2023, 16:38
Lecture 02 -Part1- Principal component analysis
27.09.2023, 16:38
Lecture 01 -Part2- Power method and PageRank
19.09.2023, 10:28
Lecture 01 -Part1- Power method and PageRank
19.09.2023, 10:28
Media
PHYS-231 Science des données
Lecture 14 -Part2 - Phase transitions (2024)
17.12.2024, 11:13
Lecture 14 -Part1 - Phase transitions (2024)
17.12.2024, 11:11
Lecture 04 -Part2 - Linear Regression Continued (2024)
01.10.2024, 11:00
Lecture 04 -Part1 - Linear Regression Continued (2024)
01.10.2024, 10:58
Lecture 14 -Part2 - Phase transitions
19.12.2023, 10:36
Lecture 14 -Part1 - Phase transitions
19.12.2023, 09:07
Lecture 13 -Part2 - Theory and examples of MCMC
12.12.2023, 10:07
Lecture 13 -Part1 - Theory and examples of MCMC
12.12.2023, 09:05
Lecture 12 -Part2 - Sampling with Monte Carlo Markov Chains
05.12.2023, 10:05
Lecture 12 -Part1 - Sampling with Monte Carlo Markov Chains
05.12.2023, 09:02
Lecture 11 -Part2 - Brownian motion, diffusion, random walks and maximum entropy principle
28.11.2023, 10:06
Lecture 11 -Part1 - Brownian motion, diffusion, random walks and maximum entropy principle
28.11.2023, 09:06
Lecture 10 -Part2 - Back to linear regression, estimation of the errors
21.11.2023, 15:57
Lecture 10 -Part1 - Back to linear regression, estimation of the errors
21.11.2023, 15:56
Lecture 09 -Part2 - Maximum Likelihood, examples and theory
14.11.2023, 10:35
Lecture 09 -Part1 - Maximum likelihood, examples and theory
14.11.2023, 10:35
Lecture 08 -Part2 - Central limit theorem, statistics and maximum likelihood
07.11.2023, 10:14
Lecture 08 -Part1 - Central limit theorem, statistics and maximum likelihood
07.11.2023, 10:12
Lecture 07 -Part2- Propagation of uncertainty II
31.10.2023, 15:26
Lecture 07 -Part1- Propagation of uncertainty II
31.10.2023, 15:23
Lecture 06 -Part2- Generation of random variables and propagation of uncertainty I
24.10.2023, 14:24
Lecture 06 -Part1- Generation of random variables and propagation of uncertainty I
24.10.2023, 14:22
Lecture 05 -Part2- Gradient descent
17.10.2023, 10:54
Lecture 05 -Part1- Gradient descent
17.10.2023, 10:37
Lecture 04 -Part2- Linear regression continued
10.10.2023, 10:55
Linear regression and gradient descent
Lecture 03 -Part2- Linear regression and least squares method
05.10.2023, 13:10
Lecture 03 -Part1- Linear regression and least squares method
05.10.2023, 13:07
Lecture 02 -Part2- Principal component analysis
27.09.2023, 16:38
Lecture 02 -Part1- Principal component analysis
27.09.2023, 16:38
Lecture 01 -Part2- Power method and PageRank
19.09.2023, 10:28
Lecture 01 -Part1- Power method and PageRank
19.09.2023, 10:28
Enregistrement du cours sur Mediaspace: https://mediaspace.epfl.ch/channel/PHYS-231+Science+des+donn%C3%A9es/54036?
- Announcements (Forum)
- Q&A (Forum)
- Anonymous Questions and Feedback (Feedback)
- Notes de cours (File)
- Mock Exam Questions (File)
- Answers to mock exam questions (File)
- Examen 2023-2024 (File)
- Examen 2023-2024 (version anglaise) (File)
- Examen 2023-2024 solutions (File)
- Diapositives d'introduction (File)
- Info about teacher dashboard (File)
- Exercise 1 - python and numpy (File)
- Solutions Exercise 1 (File)
- Exercise 5 - Gradient Descent, Logistic Regression and Introduction to Neural Nets (File)
- Solutions Exercise 5 (File)
- Graded exercise 1 : regression and gradient descent (File)
- train.csv (File)
- Solution graded exercise 1 (File)
28 October - 3 November
4 November - 10 November
11 November - 17 November
18 November - 24 November
25 November - 1 December
2 December - 8 December
9 December - 15 December
- Graded exercise 2 (File)
- Mock graded exercise: Sampling in high dimension (File)
- Mock Graded exercise: solution (File)