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forecasting:meeting_minutes_september_27_2016 [2016/09/28 03:01] jobatake |
forecasting:meeting_minutes_september_27_2016 [2021/09/19 21:59] (current) |
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| ====== Forecasting Meeting Minutes for Week of September 27, 2016 ====== | ====== Forecasting Meeting Minutes for Week of September 27, 2016 ====== | ||
| - | ** Present: Gordon, Brieanna, Jaimie ** | + | ** Present Tuesday: Gordon, Brieanna, Jaimie ** |
| + | |||
| + | ** Present Thursday: Gordon, Brieanna, Jaimie, Austin ** | ||
| ===== Updates ===== | ===== Updates ===== | ||
| * More coordination with Seyyed | * More coordination with Seyyed | ||
| + | |||
| + | ===== Meeting with Dr. Kuh ===== | ||
| ===== Expectations ===== | ===== Expectations ===== | ||
| * 1. Well documented code | * 1. Well documented code | ||
| - | * 2. Learning abt signal processing/machine learning | + | * Inputs/Outputs: |
| + | * Mean Squared Error | ||
| + | * Input | ||
| + | * Weight of length | ||
| + | * 2. Learning signal processing/machine learning algorithms | ||
| * Reproducing what Masaki did | * Reproducing what Masaki did | ||
| * Visualizing Data w/ 3D plotting: | * Visualizing Data w/ 3D plotting: | ||
| Line 21: | Line 30: | ||
| * Reserve or storage energy | * Reserve or storage energy | ||
| * Shift demands | * 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 ===== | ===== Reminders ===== | ||