COT5615-Math-for-Intelligent-Systems-Fall-2018

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

1. Vector Spaces
2. Linear Algebra
3. Principal component Analysis for dimensionality reduction
4. Singular Value Decomposition
5. Simple linear binary and multi-class classifier
6. Hilbert Spaces
7. Convolution and Fourier Transform
8. Basics of Convolution Neural Networks
9. Back propagation.
10. Lagrange Optimization
11. Statistical mechanics 001 (Gibbs free energy, entropy)
12. Convex functions and divergences (Bregman divergence and KL divergence)
13. Different Distributions (Gaussian and Binomial)
14. Independent and Identically Distributed and Maximum Likelihood