Date archives "April 2018"

STAT 306 – Finding Relationships in Data Course Reflection

I also thought that the topics covered in this course will be very relevant at my future job. A lot of the course was done in R. There was an interesting homework assignment where he gave the entire class the same data set. Whoever was able to get the lowest Mean Squared Prediction Error with their model would get a high mark. We were able to model the data however we liked. I personally used a training/test subset approach.

Topics covered:

  • Modeling a response variable as a function of several explanatory variables
  • Multiple regression for a continuous response
  • Logistic regression for a binary response
  • Log-linear models for count data
  • Finding low-dimensional structure
  • Principal components analysis (PCA)
  • Cluster analysis

MATH 307 – Applied Linear Algebra

Definitely one of the harder courses. However, I came out with good MATLAB knowledge, which I used in other courses I was taking at the same time. The course felt like a combination of many of my past MATH courses but more in-depth and difficult.It always feels rewarding when you get to apply mathematical concepts to real world  problems like chemical systems, circuits, and Markov Chain probabilities.

Topics Covered:

  • Solving linear equations
  • Interpolation
  • Finite difference approximations
  • Subspaces, basis and dimension
  • The four fundamental subspaces for a matrix
  • Graphs and networks
  • Projections
  • Complex vector spaces and inner product
  • Orthonormal bases, orthogonal matrices and unitary matrices
  • Fourier series
  • Discrete Fourier transform
  • Eigenvalues and Eigenvectors
  • Hermitian matrices and real symmetric matrices
  • Power method
  • Recursion relations
  • Markov chains
  • Singular value decomposition
  • Principle component analyis (PCA)
  • Applications of linear algebra to problems in science and engineering
  • Use of computer algebra systems for solving problems in linear algebras