Study Notes

Welcome to my study notes repo!

Some idea that culminated out of me slowly succumbing to impostor syndrome, especially after I stumbled so much back when I was attempting my Master's degree over at Sunway. Figured that I'd take a jab at this and remake notes for some of my undergrad classes back in the day, and more.

You'll find that the subjects I make notes for may reflect those I've taken during my university studies. Do take note that they do not necessarily reflect the same curriculum being taught at my alma mater(s) at present moment, and may change without me knowing. I plan on keeping them updated as I learn much more as time goes on.

Taught by: Dr. Tay Kian Boon & Sven Schleier, William Koh & WithSecure Team

Part 1 Part 2

Taught by: Adj. Prof. Fred Ezerman & Prof. Wang Huaxiong

Introduction to Cryptography Mathematical Fundamentals Encryption Schemes and Definitions of Security Algebraic Structures and Number Theory Symmetric Encryption, AES Hash Functions, MAC Public Key Cryptography I: Introduction and Knapsacks Public Key Cryptography II: RSA Public Key Cryptography III: Rabin & Elgamal Digital Signatures Key Management Secret Sharing and Its Applications Secret Sharing for General Access Structures Introduction to Blockchain and Cryptocurrency Fair Exchange and Zero Knowledge Proof

NOTE: SC - Symmetric Cryptography, PKC - Public Key Cryptography

Taught by: Prof. Anwitaman Datta & Dr. Tay Kian Boon

Part 1 Part 2

Taught by: Dr. Zhang TianWei

Part 1 Part 2

Taught by: Mr. Ong Chin Ann & Dr. Gondesen Florian Max

Part 1 Part 2

Taught by: Dr. Greg Mayer

Linear Algebra I: Linear Equations

Module 1: Linear Systems & Spans Module 2: Solution Sets & Linear Independence Module 3: Linear Transforms

Linear Algebra II. Matrix Algebra

Module 4: Matrix Addition & Scalar Subtraction Module 5: The Invertible Matrix Theorem & Applications Module 6: Computer Graphics & Subspaces

Linear Algebra III. Determinants and Eigenvalues

Module 7: Determinants Module 8: Markov Chains & Eigenvalues Module 9: Diagonalization & Page Rank

Linear Algebra IV. Orthogonality & Symmetric Matrices and the SVD

Module 10: Orthogonal Projections Module 11: Gram-Schmidt Module 12: Least-Squares Module 13: Quadratic Forms & Constrained Optimization Module 14: The SVD