andy
1513f2b378
Affected files: STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Gradient Boosting Machine.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised.md STEM/AI/Classification/Supervised/README.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Learning.md STEM/AI/Neural Networks/Learning/Boltzmann.md STEM/AI/Neural Networks/Learning/Competitive Learning.md STEM/AI/Neural Networks/Learning/Credit-Assignment Problem.md STEM/AI/Neural Networks/Learning/Hebbian.md STEM/AI/Neural Networks/Learning/Learning.md STEM/AI/Neural Networks/Learning/README.md STEM/AI/Neural Networks/RNN/Autoencoder.md STEM/AI/Neural Networks/RNN/Deep Image Prior.md STEM/AI/Neural Networks/RNN/MoCo.md STEM/AI/Neural Networks/RNN/Representation Learning.md STEM/AI/Neural Networks/RNN/SimCLR.md STEM/img/comp-learning.png STEM/img/competitive-geometric.png STEM/img/confusion-matrix.png STEM/img/decision-tree.png STEM/img/deep-image-prior-arch.png STEM/img/deep-image-prior-results.png STEM/img/hebb-learning.png STEM/img/moco.png STEM/img/receiver-operator-curve.png STEM/img/reinforcement-learning.png STEM/img/rnn+autoencoder-variational.png STEM/img/rnn+autoencoder.png STEM/img/simclr.png STEM/img/sup-representation-learning.png STEM/img/svm-c.png STEM/img/svm-non-linear-project.png STEM/img/svm-non-linear-separated.png STEM/img/svm-non-linear.png STEM/img/svm-optimal-plane.png STEM/img/svm.png STEM/img/unsup-representation-learning.png
63 lines
1.7 KiB
Markdown
63 lines
1.7 KiB
Markdown
# Supervised
|
|
- Dataset with inputs manually annotated for desired output
|
|
- Desired output = supervisory signal
|
|
- Manually annotated = ground truth
|
|
- Annotated correct categories
|
|
|
|
## Split data
|
|
- Training set
|
|
- Test set
|
|
***Don't test on training data***
|
|
|
|
## Top-K Accuracy
|
|
- Whether correct answer appears in the top-k results
|
|
|
|
## Confusion Matrix
|
|
Samples described by ***feature vector***
|
|
Dataset forms a matrix
|
|
|
|
|
|
# Un-Supervised
|
|
- No example outputs given, learns how to categorise
|
|
- No teacher or critic
|
|
|
|
## Harder
|
|
- Must identify relevant distinguishing features
|
|
- Must decide on number of categories
|
|
|
|
# Reinforcement Learning
|
|
- No teacher - critic instead
|
|
- Continued interaction with the environment
|
|
- Minimise a scalar performance index
|
|
|
|
|
|
|
|
- Critic
|
|
- Converts primary reinforcement to heuristic reinforcement
|
|
- Both scalar inputs
|
|
- Delayed reinforcement
|
|
- System observes temporal sequence of stimuli
|
|
- Results in generation of heuristic reinforcement signal
|
|
- Minimise cost-to-go function
|
|
- Expectation of cumulative cost of actions taken over sequence of steps
|
|
- Instead of just immediate cost
|
|
- Earlier actions may have been good
|
|
- Identify and feedback to environment
|
|
- Closely related to dynamic programming
|
|
|
|
## Difficulties
|
|
- No teacher to provide desired response
|
|
- Must solve temporal credit assignment problem
|
|
- Need to know which actions were the good ones
|
|
|
|
# Fitting
|
|
- Over-fitting
|
|
- Classifier too specific to training set
|
|
- Can't adequately generalise
|
|
- Under-fitting
|
|
- Too general, not inferred enough detail
|
|
- Learns non-discriminative or non-desired pattern
|
|
|
|
# ROC
|
|
Receiver Operator Characteristic Curve
|
|
 |