Lectures

• Lecture 1: Introduction to Mathematics

• Lecture 2: Core Ideas from Number Theory

• Lecture 3: Information Theory

• Lecture 4: Probability, Entropy, Information Theory

• Lecture 5: Shannon's Source Coding Theorem

• Lecture 6: Source Coding

• Lecture 7: Arithmetic Coding

• Lecture 8: Channel Coding

• Lecture 9: Shannon's Noisy Channel Coding Theorem

• Lecture 10: Hamming Codes

• Lecture 11: The Art and Science of Cryptography

• Lecture 13: Introduction to Chaos

• Lecture 14: Three condition's of Chaos

• Lecture 16: Cobweb Plots, Lyapunov Exponent, Fractals

• Lecture 17: Fractals

• Lecture 18: Applications of Chaos Theory

Proofs for infinitude of Primes

• Using Topology

• Using Mersenne Numbers

• The Counting Argument

• Erdős' proof

• Using Natural Logarithm

• Using Dynamical Systems

Homework Assignments

• Homework 1

• Homework 2

• Homework 3

• Homework 4

• Homework 5

• Homework 6

• Homework 7

• Homework 8

• Homework 9

Lectures on Causality

By: Aditi Kathpalia

• Introduction to Causal Inference

• Random Variables

• Auto-Regressive Processes and Granger Causality

• Markov Processes

• Transfer Entropy