<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>State Space Models on Sange Mehrab</title><link>https://anwarshamim01.github.io/Sang_e_Mehrab/tags/state-space-models/</link><description>Recent content in State Space Models on Sange Mehrab</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://anwarshamim01.github.io/Sang_e_Mehrab/tags/state-space-models/index.xml" rel="self" type="application/rss+xml"/><item><title>1.5 Mathematics of Machine Learning and Deep Learning for Time Series</title><link>https://anwarshamim01.github.io/Sang_e_Mehrab/courses/course/chapter-01/section-05/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://anwarshamim01.github.io/Sang_e_Mehrab/courses/course/chapter-01/section-05/</guid><description>A beginner-to-advanced mathematical introduction to time-series machine learning and deep learning, covering classical forecasting, stochastic processes, spectral analysis, supervised time-series learning, recurrent neural networks, LSTMs, GRUs, temporal convolutional networks, transformers, PatchTST, iTransformer, TimesNet, TimeMixer, state-space models, S4, Mamba, DeepAR, N-BEATS, N-HiTS, Temporal Fusion Transformers, diffusion-based forecasting, neural ODEs, neural CDEs, foundation models, probabilistic losses, conformal prediction, anomaly detection, classification, and imputation.</description></item></channel></rss>