Random Walks in Computational Thinking - Lecture 12
Explore random walks in computational thinking, covering visualization, Julia concepts, benchmarking, and advanced topics like mutable structs and type parameters.
Random Variables as Types - Lecture 11
Explore random variables as types in Julia programming, covering Gaussian distributions, sampling, abstract types, and generic programming for probability distributions.
Sampling and Random Variables in Julia - Lecture 9
Explore random sampling, probability distributions, and data visualization techniques using Julia, covering topics from basic coin tosses to complex probability densities and histogram normalization.
Principal Component Analysis - Lecture 8
Explore Principal Component Analysis: from matrix rank to SVD. Learn to measure data cloud size, understand correlated data, and navigate high-dimensional spaces using statistical techniques.
Transformations - Composability and Linearity in Computational Thinking - Lecture 4
Explore composability and linearity in transformations, focusing on image processing applications and advanced Julia programming techniques for vector-valued functions and linear/nonlinear transformations.
Transformations and Automatic Differentiation in Computational Thinking - Lecture 3
Explore transformations and automatic differentiation in Julia, covering linear and non-linear transformations, function types, and applications in machine learning, with practical examples and visualizations.
Transforming Images and Image Processing Techniques - Lecture 2
Explore image transformation techniques using Julia, including downsampling, linear combinations, convolutions, and filters. Learn about complexity, GPUs, and data structures in computational thinking.
Matrix Calculus for Linear Algebra - MIT 18.06 Spring 2020
Comprehensive exploration of matrix calculus, covering gradients, Jacobians, and advanced concepts. Ideal for those seeking a deep understanding of mathematical foundations in linear algebra.
Understanding the Discrete Fourier Transform - Week 14
Explore Discrete Fourier Transform using Julia, from basic concepts to implementation. Compare DFT with Fast Fourier Transform, analyze sounds, and understand computational complexity.
Macroscopic Models of Epidemic Dynamics - Week 6
Explore macroscopic epidemic models, transitioning from stochastic to deterministic approaches. Learn about SIR models, continuous-time dynamics, and differential equations in epidemiology.
Graphs, Trees, and Spreading Disease - MIT 18.S191 Fall 2020 Week 5
Explore graph theory, tree traversal, and epidemic modeling using Julia programming, with hands-on demonstrations and practical applications in network analysis and disease spread simulation.
Probability via Computation - Week 4
Explore probability concepts through computational methods, covering random sampling, Monte Carlo simulations, Bernoulli trials, and the Central Limit Theorem with practical examples and visualizations.
Understanding Structure in Data - Week 3
Explore data structure analysis using Julia, covering matrix rank, data visualization, PCA, and building a simple recommendation engine. Gain practical insights into advanced data science concepts.
Structure and Matrices in Julia Programming - Lecture 3
Explore matrices, sparse structures, and SVD for image compression and computational thinking in Julia, with practical applications in machine learning and linear algebra.
Adventures in Computing - From Punch Cards to Differential Equations
Prof Linda Petzold explores her journey in computational science, from punch cards to modern differential equations, highlighting key developments in numerical methods, stochastic simulations, and their applications in various fields.
Maximising the Accuracy of Hydraulic Models
Explore factors influencing hydraulic model accuracy, from input data to computational solvers. Learn to maximize model reliability and confidence, especially when calibration data is unavailable.
Tricks and Tips in Numerical Computing - Keynote
Explore advanced numerical computing techniques, from complex differentiation to low-precision arithmetic, with insights on error analysis, matrix operations, and IEEE standards for improved computational accuracy and efficiency.
Celeste.jl - Petascale Computing in Julia
Explore Celeste.jl, a Julia-based project for petascale astronomical image analysis using Bayesian inference, showcasing Julia's power in high-performance computing and data science on supercomputers.
MODFLOW 6 and FloPy
Learn to create and analyze groundwater models using MODFLOW 6 and FloPy in Python. Covers model setup, visualization, water balance, pathlines, and transient simulations.
Monday.com Tutorial for Beginners - How to Get Started with Monday
Comprehensive guide to Monday.com's project management features, covering setup, interface, workspaces, boards, items, dashboards, automations, and essential functions for beginners.
3D CSS - Lighting, Animations, and More
Learn advanced 3D CSS techniques including lighting, animations, and perspective. Create interactive scenes with bouncing balls, rotating cubes, and dynamic shadows for engaging web designs.