Depth of Field

    It is the range of object distances over which the image is "sufficiently well" focused, i.e. the range over which the blur(b) is less than the pixel size of a camera sensor.

(Taken from a YouTube video of Shree K. Nayar from Columbia University)

Unsupervised Learning in Bioinformatics

    There are a few methods in machine learning that can be applied in the field of bioinformatics like studying a genome or classification of different proteins/sub-genes in a gene. Such methods known to me are classified under unsupervised learning approaches and are listed as follows:

1) Hierarchical Clustering (Dendrograms).

2) Matrix Factorization.

Eigenfaces: An application of Principal Component Analysis (precisely Dimensionality Reduction)

(The above image represents 16 eigenfaces for about a 1000 different images of human faces. Image Source: same link as for "towardsdatascience" page mentioned below.)

    I just came across the concept of having human faces as eigen vectors and this is simply awesome! This concept can be used to store compressed version of individual human faces(this saves storage space) and those individual faces can still be reconstructed to have a good approximation of their original versions. All this can be done using the basic concept of machine learning called Principal Component Analysis! Please checkout this crisp explanation from "towardsdatascience" page to know more about this topic.

Link: https://towardsdatascience.com/eigenfaces-recovering-humans-from-ghosts-17606c328184

Interpretability v/s Flexibility for different statistical learning methods

 

(Source: An Intoduction to Statistical Learning with Applications in R written by James G., Witten D., Hastie T. and Tibshirani R. and published by Springer Text in Statistics. This is a part of my machine learning specialization course from CU Boulder.)

    I learned from this topic that we can choose a method from multiple statistical learning topics based on our goal. This book shares an example saying that if we are interested in predicting the accurate values in the stock market and not much concerned about inference (how each predictor variable is associated with the output), we can use high-flexibility approaches like deep learning. However, if we are concerned about the inference, we can use high-interpretability models like Lasso or Least-squares. The book also states that sometimes the high-interpretability models give more accurate results.

Birefringence

    Aside from dispersion, materials usually have the same index of refraction for all orientations of the electric field in the light. However, some materials, usually nearly pure crystals, have two indices of refraction for the same wavelength of light. The difference is due only to the orientation of the light's electric field. Therefore, light with its electric field oscillating in the vertical direction experiences a different index of refraction from the index of refraction for an electric field oscillating in the horizontal direction. This is called birefringence. (Source: Optics for Dummies by Duree)

Variables in a Linear Regression model (machine learning)

There are two types of variables in a dataset in general:
1) Real valued (ex.: 1,2,3,4)
2) Categorical (ex.: Blood Group)
    Categorical variables have two sub-types:
    a) Ordinal (ex.: Age group 25-55, or Grades A-D)
    b) Non-ordinal (ex.: Male/Female, Plant/Animal)

    It is comparatively easy to build linear regression model around real-values and ordinal-categorical variables that can relate to regression. However, nonordinal-categorical variables pose a problem for a "regression" model. This is  because the regression equations,

y = a0 + a1X1 + a2X2 +....

cannot rely on their arbitrary nature to predict output values. Nonetheless, these nonordinal categorical variables can always be transformed into real-valued or binary forms to make use of them in a regression problem.

Mangin Mirror to Resolve Spherical Aberration

Most imaging systems face the problem of spherical aberration when trying to focus light on an image plane. A Mangin mirror, which is a mirror with a positive focal length (concave shape for mirrors) when placed alongside a lens with negative meniscus, is known to resolve for spherical aberration.

Figure 1. Mangin mirror with a negative meniscus lens in blue color and the mirror in red color. (Source: Wikipedia)