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changes.mady.by.user Jahanvi Ojha

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changes.mady.by.user Akanksha Singh

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Volunteers


NameML Category
Jahanvi                                  Supervised                                        
AkankshaUnsupervised
Kanak RajReinforced

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NameComments on ApplicabilityReference









Un-supervised

  1. Clustering
  2. Anomaly Detection
  3. Dimensionality Reduction

Algorithms


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NameComments on ApplicabilityReference
Hierarchical Clustering
  1. (N-1) combination of clusters are formed to choose from.
  2. Expensive and slow. n×n  distance matrix needs to be made.
  3. Cannot work on very large datasets.
  4. Results are reproducible.
  5. Does not work well with hyper-spherical clusters.
  6. Can provide insights into the way the data pts. are clustered.
  7. Can use various linkage methods(apart from centroid).

k-means



Reinforcement Learning

  1. Active Learning
  2. No labeled data
  3. No supervisor, only  reward
  4. Actions are sequential
  5. Feedback is delayed, not instantaneous.
  6. Can afford to make mistakes?
  7. Is it possible to use a simulated environment for the task?
  8. Lots of time
  9. Think about the variables that can define the state of the environment.
    1. State Variables and Quantify them
    2. The agent has access to these variables at every time step
    3. Concrete Reward Function and Compute Reward after action
    4. Define Policy Function

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