Useful resources for my courses at University Of Edinburgh
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Writing / Research Skills
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University Of Edinburgh
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Tools
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Books
###General
- YoutTube: mathematicalmonk
- Stanford: Machine Learning by Andrew Ng
- University of Toronto: Neural Networks for Machine Learning by Geoffrey Hinton
- University of British Columbia: Machine Learning
- Stanford: Machine Learning (old)
- California Institute of Technology: Learning From Data
###MATLAB
###WEKA
- !!! Bayesian Methods Tutorial
- Introduction to Bayesian Learning
- (INTUITIVE !!) Explanation: Naive Bayes
- FAQ
- User's guide to Support Vector Machines
- Support Vector Machines Explained
- A Practical Guide to Support Vector Classi cation
- RBF SVM parameters
- SVMs and Classification using WEKA
- !? Kernel Functions for Machine Learning Applications
- SVM with polynomial kernel visualization
- An Idiot’s guide to Support vectormachines (SVMs)
- Making sense of principal component analysis, eigenvectors & eigenvalues
- A tutorial on Principal Components Analysis
- PCA and SVMs
- Slides: Decision Trees
- Different Classification Methods Explained
- Probabilistic Models for Unsupervised Learning
- Bernoulli, Beta, Cojugate Priors, MATLAB examples
- Monte Carlo introduction
- Machine Learning Lecture Notes (Toronto)
- slides..
- Mark V Shaney text generation
- Feature Extraction
- MCMC: (Course: Randomness and Computation)
- !!!! MCMC:Metropolis Algorithm
- !! How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
- !! An Introduction to MCMC for Machine Learning
- "Sampling (that is Monte Carlo) is to Bayesian Inference, what Optimization is to Maximum Likelihood"
- Monte Carlo
- Lecture notes: Monta Carlo Methods
- Metropolis-Hastings in R
- Visualizing Metropolis-Hastings in R
- The Metropolis-Hastings algorithm by example
- Understanding Metropolis-Hastings
- Metropolis-Hastings and Gibbs Sampling
- Notes: Metropolis-Hastings
- Implementation of stochastic simulation algorithms: Metropolis-Hastings/Gibbs/...
- !! Metropolis-Hastings Code
- Graph Databases
- DOC: Hadoop Streaming
- Matrix Transpose I
- Matrix Transpose II
- Secondary Sort I
- Secondary Sort II
- Explanatory Illustration
- Network analysis with Hadoop and neo4j
- Graph Databases: Trends in the web of Data
- noSQL Data Modeling
- neo4j Papers/Resources
- neo4j vs Hadoop
- InterSocialDB: An Infrastructure for Managing Social Data
- noSQL and MapReduce
- does MapReduce make noSQL scalable?
- ! intro to graph databases
- ! Modelling with Graphs
- ! How to create an interest graph
- Gravity.com Interest Graph
- social vs interest Graph
- architectue: social interest discovery
- social vs interest graph
- ! Concepts with Code
- ! Lecture Notes !
- Phong Shading
- Refraction
- Anti Aliasing
- Lighting
- Ray Tracing Fomulas
- Ray - Triangle Intersection
- Ray - AABB Intersection
- Ray - Cylinder Intersection
- Slides: Advanced Ray Tracing
- Slides: Bouning Volume Hierarchies
- Quad Trees: some code
- Octree traversal/Ray-AABB intersection
- flipcode.com: Spatial_Subdivisions
- flipcode.com: Soft Shadows
- Glass: Fresnel