Course Outline


Probability Theory, Model Selection, Decision and Information Theory

Probability Distributions

Linear Models for Regression and Classification

Neural Networks

Kernel Methods

Sparse Kernel Machines

Graphical Models

Mixture Models and EM

Approximate Inference

Sampling Methods

Continuous Latent Variables

Sequential Data

Combining Models

Summary and Conclusion


  • Understanding of statistics.
  • Familiarity with multivariate calculus and basic linear algebra.
  • Some experience with probabilities.


  • Data analysts
  • PhD students, researchers and practitioners
 21 Hours

Number of participants

Price per participant

Testimonials (3)

Related Courses

OpenNN: Implementing Neural Networks

14 Hours

Pattern Matching

14 Hours

Artificial Intelligence (AI) in Automotive

14 Hours

Artificial Intelligence (AI) Overview

7 Hours

From Zero to AI

35 Hours

Artificial Neural Networks, Machine Learning, Deep Thinking

21 Hours

Applied AI from Scratch

28 Hours

Applied AI from Scratch in Python

28 Hours

Applied Machine Learning

14 Hours

Artificial Neural Networks, Machine Learning and Deep Thinking

21 Hours

Deep Learning Neural Networks with Chainer

14 Hours

Deep Reinforcement Learning with Python

21 Hours

Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

21 Hours

Matlab for Deep Learning

14 Hours

Artificial Intelligence (AI) for Mechatronics

21 Hours

Related Categories