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Supervised

Supervised learning algorithms make predictions based on a set of examples
Classification: When the data are being used to predict a categorical variable, supervised learning is also called classification. This is the case when assigning a label or indicator, either dog or cat to an image. When there are only two labels, this is called binary classification. When there are more than two categories, the problems are called multi-class classification.
Regression: When predicting continuous values, the problems become a regression problem.
Forecasting: This is the process of making predictions about the future based on past and present data. It is most commonly used to analyze trends. A common example might be an estimation of the next year sales based on the sales of the current year and previous years.

In progress**

Clustering - hierarchical clustering, k-means, mixture models, DBSCAN, and OPTICS algorithm
Anomaly Detection - Local Outlier Factor, and Isolation Forest
Dimensionality Reduction - Principal component analysis, Independent component analysis, Non-negative matrix factorization, Singular value decomposition

Name	Comments on Applicability	Reference
Hierarchical Clustering	(N-1) combination of clusters are formed to choose from. Expensive and slow. n×n distance matrix needs to be made. Cannot work on very large datasets. Results are reproducible. Does not work well with hyper-spherical clusters. Can provide insights into the way the data pts. are clustered. Can use various linkage methods(apart from centroid).
k-means	Pre-specified number of clusters. Less computationally intensive. Suited for large dataset. Point of start can be random which leads to a different result each time the algorithm runs. K-means needs circular data. Hyper-spherical clusters. K-Means simply divides data into mutually exclusive subsets without giving much insight into the process of division. K-Means uses median or mean to compute centroid for representing cluster.
Gaussian Mixture Models

Model-Free vs Model-Based RL

Whether the agent has access to (or learns) a model of the environment(a function that predicts state transitions and rewards)

Model Free	Model-Based
forego the potential gains in sample efficiency from using a model	Allows to plan ahead and look in possible results for a range of possible choices.
easier to implement and tune.	Ground Truth Model for any task is generally not available.
	If agents want to use a model then it has to prepare it purely from experience
	fundamentally hard
	being willing to throw lots of time
	High computation
	Can fail off due to over-exploitation of bias

What to Learn in Model-Free RL

Q-Learning

Policy Optimization	Q-Learning
optimize the parameters either directly by gradient ascent on the performance objective or indirectly, by maximizing local approximations	learn an approximator for the optimal action-value function
performed on-policy, each update only uses data collected while acting according to the most recent version of the policy	performed off-policy, each update can use data collected at any point during training
directly optimize for the thing you want	indirectly optimize for agent performance
More stable	tends to be less stable
advantage of being substantially more sample efficient when they do work, because they can reuse data more effectively	Less sample efficient and takes longer to learn as learning data is limited at every iteration.