Present Tuesday: Gordon, Brieanna, Jaimie

Present Thursday: Gordon, Brieanna, Jaimie, Austin

• More coordination with Seyyed

• 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

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

• Worked on implementation of Recursive Algorithm
• Compared computation time with Regression
  • Derived relationship between samples and computation time for algorithms

Contributing authors:

Created by jobatake on 2016/09/28 02:58.