mathematicalmonk

(ML 1.1) Machine learning - overview and applications

(ML 1.2) What is supervised learning?

(ML 1.3) What is unsupervised learning?

(ML 1.4) Variations on supervised and unsupervised

(ML 1.5) Generative vs discriminative models

(ML 1.6) k-Nearest Neighbor classification algorithm

(ML 2.1) Classification trees (CART)

(ML 2.2) Regression trees (CART)

(ML 2.3) Growing a regression tree (CART)

(ML 2.4) Growing a classification tree (CART)

(ML 2.5) Generalizations for trees (CART)

(ML 2.6) Bootstrap aggregation (Bagging)

(ML 19.4) Examples of Gaussian processes (part 2)

(IC 4.2) Huffman coding - more examples

(ML 9.7) Basis functions MLE

(ML 18.2) Ergodic theorem for Markov chains

(ML 19.7) Operations preserving positive semidefinite kernels

(IC 5.7) Decoder for arithmetic coding (infinite-precision)

(ML 14.11) Viterbi algorithm (part 1)

(IC 5.10) Generalizing arithmetic coding to non-i.i.d. models

(ML 18.3) Stationary distributions, Irreducibility, and Aperiodicity

(IC 5.9) Computational complexity of arithmetic coding

(ML 14.5) Hidden Markov models (HMMs) (part 2)

(ML 3.1) Decision theory (Basic Framework)

(PP 5.5) Law of large numbers and Central limit theorem

(ML 18.1) Markov chain Monte Carlo (MCMC) introduction

(IC 2.7) Expected codeword length

(IC 5.11) Finite-precision arithmetic coding - Rescaling

(IC 5.13) Finite-precision arithmetic coding - Encoder

(ML 15.1) Newton's method (for optimization) - intuition

(IC 2.3) Symbol codes - definition and examples

(IC 5.5) Rescaling operations for arithmetic coding

(PP 3.1) Random Variables - Definition and CDF

(IC 5.1) Arithmetic coding - introduction

(ML 10.1) Bayesian Linear Regression

(PP 3.2) Types of Random Variables

(IC 4.6) Optimality of Huffman codes (part 1) - inverse ordering

(ML 8.1) Naive Bayes classification

(IC 4.9) Optimality of Huffman codes (part 4) - extension and contraction