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Probability Primer
- (PP 1.1) Measure theory: Why measure theory - The Banach-Tarski Paradox
- (PP 1.2) Measure theory: Sigma-algebras
- (PP 1.3) Measure theory: Measures
- (PP 1.4) Measure theory: Examples of Measures
- (PP 1.5) Measure theory: Basic Properties of Measures
- (PP 1.6) Measure theory: Basic Properties of Measures (continued)
- (PP 1.7) Measure theory: More Properties of Probability Measures
- (PP 1.8) Measure theory: CDFs and Borel Probability Measures
- (PP 1.R) References for Probability and Measure theory
- (PP 1.S) Measure theory: Summary
- (PP 2.1) Conditional Probability
- (PP 2.2) Independence
Machine Learning
- (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 2.6) Bootstrap aggregation (Bagging)
- (ML 2.7) Bagging for classification
- (ML 2.8) Random forests
Information Theory
- (IC 1.1) Information theory and Coding - Outline of topics
- (IC 1.2) Applications of Compression codes
- (IC 1.3) Applications of Error-correcting codes
- (IC 1.4) Source-channel separation
- (IC 1.5) Examples of source-encoder-channel pipelines
- (IC 1.6) A different notion of "information"
- (IC 2.1) A puzzle on weighing coins
- (IC 2.2) Symbol codes - terminology and notation
- (IC 2.3) Symbol codes - definition and examples
- (IC 2.4) Decoding - prefix versus non-prefix
- (IC 2.5) Prefix codes
- (IC 2.6) Prefix codes - remarks and what's next
Recent uploads
- (IC 5.14) Finite-precision arithmetic coding - Decoder
- (IC 5.13) Finite-precision arithmetic coding - Encoder
- (IC 5.12) Finite-precision arithmetic coding - Setup
- (IC 5.11) Finite-precision arithmetic coding - Rescaling
- (IC 5.10) Generalizing arithmetic coding to non-i.i.d. models
- (IC 5.9) Computational complexity of arithmetic coding
- (IC 5.8) Near optimality of arithmetic coding
- (IC 5.7) Decoder for arithmetic coding (infinite-precision)
- (IC 5.4) Why the interval needs to be completely contained
- (IC 5.6) Encoder for arithmetic coding (infinite-precision)
- (IC 5.5) Rescaling operations for arithmetic coding
- (IC 5.3) Arithmetic coding - Example #2
Popular uploads
- (ML 4.1) Maximum Likelihood Estimation (MLE) (part 1)
- (ML 14.4) Hidden Markov models (HMMs) (part 1)
- (PP 1.1) Measure theory: Why measure theory - The Banach-Tarski Paradox
- (ML 1.1) Machine learning - overview and applications
- (IC 4.1) Huffman coding - introduction and example
- (ML 1.6) k-Nearest Neighbor classification algorithm
- (PP 1.2) Measure theory: Sigma-algebras
- (ML 14.2) Markov chains (discrete-time) (part 1)
- (ML 14.1) Markov models - motivating examples