Browse Source

update with what we have been doing.

master
Charles Reid 4 years ago
parent
commit
211b7fe916
  1. 37
      MachineLearning.md
  2. 15
      README.md
  3. 3
      ThePlan.md

37
MachineLearning.md

@ -67,7 +67,7 @@ List of topics focusing on theoretical components:
* Nonparametric Methods
* Decision Trees
* Lienar iscrimination
* Linear discrimination
* Miultilyaer Perceptrons
* Local Models
* Kernel Machines
@ -76,7 +76,6 @@ List of topics focusing on theoretical components:
* Graphical Models
* Combining Multiple Learners
* Reinforcement Learning
* Design and Analysis of Machine Learning Experiments
@ -84,8 +83,7 @@ List of topics focusing on theoretical components:
A long and full list of types of models under each sub-heading:
* Regression
* **Modeling relationship between variables, iteratively refined using an error measure.**
* Regression: **Modeling relationship between variables, iteratively refined using an error measure.**
* Linear Regression
* Logistic Regression
* OLS (Ordinary Least Squares) Regression
@ -93,55 +91,47 @@ A long and full list of types of models under each sub-heading:
* MARS (Multivariate Adaptive Regression Splines)
* LOESS (Locally Estimated Scatterplot Smoothing)
* Instance Based
* **Build up database of data, compare new data to database; winner-take-all or memory-based learning.**
* Instance Based: **Build up database of data, compare new data to database; winner-take-all or memory-based learning.**
* k-Nearest Neighbor
* Learning Vector quantization
* Self-Organizing Map
* Localy Weighted Learning
* Regularization
* **Extension made to other methods, penalizes model complexity, favors simpler and more generalizable models.**
* Regularization: **Extension made to other methods, penalizes model complexity, favors simpler and more generalizable models.**
* Ridge Regression
* LASSO (Least Absolute Shrinkage and Selection Operator)
* Elastic Net
* LARS (Least Angle Regression)
* Decision Tree
* **Construct a model of decisions made on actual values of attributes in the data.**
* Decision Tree: **Construct a model of decisions made on actual values of attributes in the data.**
* Classification and Regression Tree
* CHAID (Chi-Squared Automatic Interaction Detection)
* Conditional Decision Trees
* Bayesian
* **Methods explicitly applying Bayes' Theorem for classification and regression problems.**
* Bayesian: **Methods explicitly applying Bayes' Theorem for classification and regression problems.**
* Naive Bayes
* Gaussian Naive Bayes
* Multinomial Naive Bayes
* Bayesian Network
* BBN (Bayesian Belief Network)
* Clustering
* **Centroid-based and hierarchical modeling approaches; groups of maximum commonality.**
* Clustering: **Centroid-based and hierarchical modeling approaches; groups of maximum commonality.**
* k-Means
* k-Medians
* Expectation Maximization
* Hierarchical Clustering
* Association Rule Algorithms
* **Extract rules that best explain relationships between variables in data.**
* Association Rule Algorithms: **Extract rules that best explain relationships between variables in data.**
* Apriori algorithm
* Eclat algorithm
* Neural Networks
* **Inspired by structure and function of biological neural networks, used ofr regression and classification problems.**
* Neural Networks: **Inspired by structure and function of biological neural networks, used ofr regression and classification problems.**
* Radial Basis Function Network (RBFN)
* Perceptron
* Back-Propagation
* Hopfield Network
* Deep Learning
* **Neural networks that exploit cheap and abundant computational power; semi-supervised, lots of data.**
* Deep Learning: **Neural networks that exploit cheap and abundant computational power; semi-supervised, lots of data.**
* Convolutional Neural Network (CNN)
* Recurrent Neural Network (RNN)
* Long-Short-Term Memory Network (LSTM)
@ -149,8 +139,7 @@ A long and full list of types of models under each sub-heading:
* Deep Belief Network (DBN)
* Stacked Auto-Encoders
* Dimensionality Reduction
* **Find inherent structure in data, in an unsupervised manner, to describe data using less information.**
* Dimensionality Reduction: **Find inherent structure in data, in an unsupervised manner, to describe data using less information.**
* PCA
* t-SNE
* PLS (Partial Least Squares Regression)
@ -164,8 +153,7 @@ A long and full list of types of models under each sub-heading:
* Regularized Discriminant Analysis
* Linear Discriminant Analysis
* Ensemble
* **Models composed of multiple weaker models, independently trained, that provide a combined prediction.**
* Ensemble: **Models composed of multiple weaker models, independently trained, that provide a combined prediction.**
* Random Forest
* Gradient Boosting Machines (GBM)
* Boosting
@ -174,4 +162,3 @@ A long and full list of types of models under each sub-heading:
* Stacked Generalization (Blending)
* Gradient Boosted Regression Trees

15
README.md

@ -15,22 +15,13 @@ See [ThePlan.md](/ThePlan.md)
### Software Engineering
The basic concepts, the full topic list, the to do list.
Full list of software engineering topics: [SoftwareEngineering.md](/SoftwareEngineering.md)
See [SoftwareEngineering.md](/SoftwareEngineering.md)
### TODO Software Engineering
See [TODOSoftwareEngineering.md](/TODOSoftwareEngineering.md)
Schedule and task list (ended August 2017): [TODOSoftwareEngineering.md](/TODOSoftwareEngineering.md)
--------
### Machine Learning
The basic concepts and the detailed topic list.
See [MachineLearning.md](/MachineLearning.md)
### TODO Machine Learning
Full list of machine learning topics: [MachineLearning.md](/MachineLearning.md)
See [TODOMachineLearning.md](/TODOMachineLearning.md)

3
ThePlan.md

@ -58,8 +58,5 @@ Code:
- [C](https://git.charlesreid1.com/cs/c)
- [C++](https://git.charlesreid1.com/cs/cpp)
Practice writing out on a whiteboard and/or on paper,
before implementing on computer.
Get a big drawing pad from the art store.

Loading…
Cancel
Save