Study Report

My Learning Journey

Fluent Python (1% complete)

Algorithms by Robert Sedgewick, Kevin Wayne (0% complete)

Started working more with algorithms, a topic I’m still exploring for my research at Bern Uni. That is why I’ve added Algorithms by Robert Sedgewick, Kevin Wayne to my reading list, but the chapters are extensive, so I’ll likely progress at a slower pace.

I've decided to start reading Fluent Python to improve my coding skills in this programming language. I will try to cover a Chapter/Week
Fluent Python: Read chapter one on dunder methods
Class 3 for the Computer Vision course
Class 3 for the Machine Learning course
Read about HAC and (1+e)HAC algorithms to cluster large graphs

Attended Class 2 for the Computer Vision course Covered lens camera, Depth of Field, Lens flaws, Bay Array, desmosaicing, color moiré. Learned about based camera, image filtering (denoising, gaussian kernel and convolution)
In Class 2 for the Machine Learning course, I learned about matrix derivatives, the trace operator, and the probabilistic interpretation of linear models. We also covered the likelihood function and maximum likelihood estimation.
Began reading about algorithms, focusing on Hierarchical Agglomerative Clustering methods.

Attended Class 1 for the Computer Vision course: Covered projection models, the pinhole camera, 3D-2D projection, and vanishing points.
Attended Class 1 for the Machine Learning course : Introduced various learning algorithms, including linear regression and the Least Means Squares algorithm.
Class 1 of Applied Optimization: Covered the fundamentals of optimization problems, including linear and non-linear programs, convex optimization, and linear least squares.
Wrote a TMAP blog post , learning about Min-Hashing, LSH, and k-NN graphs in the process.