http://etiennechassaing.com/tags/ai/python/AI/PythonHugo Blox Builder (https://hugoblox.com)en-usFri, 01 Sep 2023 00:00:00 +0000http://etiennechassaing.com/media/icon_hub84619f942fe7a5590e3fd5bc84e624a_336037_512x512_fill_lanczos_center_3.pnghttp://etiennechassaing.com/tags/ai/python/http://etiennechassaing.com/teaching/foundations-of-ai/Fri, 01 Sep 2023 00:00:00 +0000http://etiennechassaing.com/teaching/foundations-of-ai/<p><img src="featured.png" alt="Image alt"> <em>AI taxonomy, source Google</em></p> <h2 id="summary">Summary</h2> <p>This course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanical engineering applications.</p> <h2 id="content">Content</h2> <p>Tools: Supervised learning: regression and classification</p> <p>Unsupervised learning: singular value decomposition, K-means</p> <p>Deep learning: brief introduction to neural networks</p> <p>Reinforcement learning: brief introduction to policy gradient method</p> <p>Theory Optimization: role of convexity, gradient descent, least-squares</p> <p>Statistics: Bayesian approach, bias and variance trade-off</p> <p>Source : <a href="https://edu.epfl.ch/coursebook/en/foundations-of-artificial-intelligence-ME-390">EPFL</a></p>