Lectures related to Compression: Prediction

Covers the concept of compression and constructing prefix-free codes based on given information.

Compression: Strong Connection and Prefix-Free Codes

Explores the relationship between code word length and probability distribution, focusing on designing prefix-free codes for efficient compression.

Compression: Prefix-Free Codes

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Distributions and Derivatives

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Compression: Kraft Inequality

Explains compression and Kraft inequality in codes and sequences.

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Probability and Statistics

Covers probability distributions, moments, and continuous random variables.

Source Coding Theorems: Entropy and Source Models

Covers source coding theorems, entropy, and various source models in information theory.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Data Compression and Shannon's Theorem: Entropy Calculation Example

Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.

Distributions & Interpolation Spaces

Covers distributions, interpolation spaces, convergence, and the concept of dual spaces.

JPEG XS & JPEG XL: Next-Gen Image Compression

Explores the cutting-edge JPEG XS and JPEG XL image compression standards, emphasizing their efficiency and versatility in various applications.

Entropy and Algorithms: Applications in Sorting and Weighing

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Data Compression and Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

Generalization Error

Discusses mutual information, data processing inequality, and properties related to leakage in discrete systems.

Source Coding Theorem

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Entropy and Compression I

Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.