Update README.md

Author: r0f1

README.md

@@ -283,10 +283,13 @@ Understanding SVM Regression: [slides](https://cs.adelaide.edu.au/~chhshen/teach
 #### Multi-label classification
 [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn) - Multi-label classification, [talk](https://www.youtube.com/watch?v=m-tAASQA7XQ&t=18m57s).  
 
+#### Signal Processing and Filtering
+[Kalman Filter book](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) - Focuses on intuition using Jupyter Notebooks. Includes Baysian and various Kalman filters.  
+[Interactive Tool](https://fiiir.com/) for FIR and IIR filters, [Examples](https://plot.ly/python/fft-filters/).  
+[The Scientist & Engineer's Guide to Digital Signal Processing (1999)](https://www.analog.com/en/education/education-library/scientist_engineers_guide.html).  
+[filterpy](https://github.com/rlabbe/filterpy) - Python Kalman filtering and optimal estimation library.  
+
 #### Time Series
-[Signal Processing Book](https://www.analog.com/en/education/education-library/scientist_engineers_guide.html)  
-Filter Design: [Article](https://tomroelandts.com/articles/how-to-create-a-simple-high-pass-filter), [Interactive Tool](https://fiiir.com/), [Filter examples](https://plot.ly/python/fft-filters/)  
-[Talk](https://www.youtube.com/watch?v=0zpg9ODE6Ww)  
 [statsmodels](https://www.statsmodels.org/dev/tsa.html) - Time series analysis, [seasonal decompose](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose.html) [example](https://gist.github.com/balzer82/5cec6ad7adc1b550e7ee), [SARIMA](https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html), [granger causality](http://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html).  
 [pyramid](https://github.com/tgsmith61591/pyramid), [pmdarima](https://github.com/tgsmith61591/pmdarima) - Wrapper for (Auto-) ARIMA.  
 [pyflux](https://github.com/RJT1990/pyflux) - Time series prediction algorithms (ARIMA, GARCH, GAS, Bayesian).