Lectures related to Approximation and Compound Interest

Explores Newton's binomial theorem and real sequences with examples and definitions.

Covers learning by rewards in deep reinforcement learning without math details.

Explores the complexity of calculating binomial coefficients using dynamic programming.

Explores the shift to deep reinforcement learning through neural networks for direct policy learning, bypassing Q-values and V-values.

Introduces combinatorics and counting methods for determining possible outcomes in various scenarios, illustrated with examples.

Explores binomial coefficient estimation, Stirling's formula, and properties of the bell curve.

Covers the binomial formula, Euler number, and infinity, including induction and convergence of sequences.

Covers the Binomial Theorem, Pascal's Identity, and Pascal's Triangle in counting.