Lectures related to Diffusion: Data Denoising and Generative Modeling

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Approximate Query Processing: BlinkDB

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Wireless Receivers: Time and Phase Offset

Covers the impact and compensation of time and phase offset in wireless receivers.

Ceramics: Powder Characterization

Explores the characterization of powders in ceramics, emphasizing the impact on ceramic properties and the manufacturing process.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Digital Signal Processing: Theory

Covers the theory of digital signal processing, including sampling, transformation methods, digitization, and PID controllers.

Pizza Making Process

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Data Representations & Processing

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Data Representations and Processing in Machine Learning

Covers data representations and processing techniques essential for effective machine learning algorithms.

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Sampling strategies

Explores research process, variable types, causality vs correlation, and sampling strategies.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Data Issues in Research

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Spatial Sampling: Concepts and Techniques

Covers spatial sampling in GIS, including autocorrelation, elevation models, and interpolation methods.