====== Forecasting Meeting Minutes for Week of September 27, 2016 ====== 
** Present Tuesday: Gordon, Brieanna, Jaimie **

** Present Thursday: Gordon, Brieanna, Jaimie, Austin **


===== Updates =====
  * More coordination with Seyyed


===== Meeting with Dr. Kuh =====
===== Expectations =====
  * 1. Well documented code
    * Inputs/Outputs:
      * Mean Squared Error
      * Input
      * Weight of length
  * 2. Learning signal processing/machine learning algorithms
  * Reproducing what Masaki did
    * Visualizing Data w/ 3D plotting:
      * Contact Masaki
  * Work on normalized data with __Zenith Angle__
  * 3. Forecast Solar Irradiance
    * Recursive least squares
    * Least mean squares
  * Machine learning aspect
  * 4. Understanding solar data
    * PV and solar data (non-linear relationship)
    * Weather effects sudden changes in weather
      * Reserve or storage energy
      * Shift demands

===== Machine Learning =====
 Know linear, unsupervised and classification
  * 2 types of algorithms:
   * Regression/Estimation
   * Classification (binary/descrete)/Detection
   * Classification: Iris Problem
     * Linear threshold function: Hyperplane separating data (0 | 1)
       * 2 items from 1 item = possible
  * Unsupervised Learning:
    * Cluster
    * PCA
  * Support Vector Machines:
    * Non-linear
  * Multi-learning networks:
    * Deep learning
  * Andrew Ng coursera

===== Group Progress =====
   * Worked on implementation of Recursive Algorithm 
   * Compared computation time with Regression
    * Derived relationship between samples and computation time for algorithms


===== Reminders =====