I took Math for Intelligent System COT5615 in fall 2018 offered by University of Florida. In this repository I am sharing the assignments, lecture PPTs and my code for the same. I have seen very common questions like "What are the topics I should go through for machine learning?", and topics of the PPTs, in my opinion will give you a clear idea about that. This was an awesome but very intensive course. Have a look!!
List of Topics covered
- Vector Spaces
- Linear Algebra
- Principal component Analysis for dimensionality reduction
- Singular Value Decomposition
- Simple linear binary and multi-class classifier
- Hilbert Spaces
- Convolution and Fourier Transform
- Basics of Convolution Neural Networks
- Back propagation.
- Lagrange Optimization
- Statistical mechanics 001 (Gibbs free energy, entropy)
- Convex functions and divergences (Bregman divergence and KL divergence)
- Different Distributions (Gaussian and Binomial)
- Independent and Identically Distributed and Maximum Likelihood