Machine Learning and It's Applications
Current Learning Background
So far, I haven't touched anything specific about machine learning, but I took three math classes as my minor electives before I graduated from ETSU and two of them somehow showed me what Machine Learning could look like.
One of them is Baysian Theory. It's more about real-world cases analysis with Baysian model and how the model changes when data builds up. We started from small variables to find the proper prior distrubutions and generate the posterior with the data and ended up with the same method on large data matrices. We did not code very much and we used R alot for convenience. And the main topics we brifly covered are Baysian linear Model, hypothesis testing, Monte Carlo integration, and Markov chain.
Another one I took is Numerical Linear Algebra and I learned the theories and how solve relevant problems by using existing functions and method in Python library. The main topics we covered are Schur decomposition, singular decoposition, principal components analysis, muti-linear regression, and some introduction about graph theories like how to divide a cluster . The projects I did, first one was to retore a blurry image by using PCA and second one was facial recognization by applying PCA on existing matrices and find the principal components of the target face.