Summaries/AI/MachineLearning/ML_Landscape.md

title	updated	created
ML_Landscape	2021-05-04 14:58:11Z	2021-05-04 14:58:11Z

ML Landscape

What is ML

Science of programming computers so they can learn from data, instead of having explicite code rules

Data examples are called training sets. Each training example is called a training instance Performance is measured in accuracy

ML is good in solving problems that either:

1. problems are too complex for traditonal approaches
2. have no known algoritme/can help find an algoritme
3. fluctuating environments: ML can learn machines to adapt to new data
4. can help humans learn to get insight in complex and large amount of data => data mining

	Description	Example
CNN	Convolutional Neural Netwerk	Image Classification
	Segmantic Segmentation	Brain scans
NLP	Natural Language Processing	News articles classification
		Text Summary
RNN	Recurring Neural Netwerk	News articles classification
NLU	Natural Language Understanding	Chatbot/personal assistant
SVM	Support Vector Machine	Forecasting
RL	Reinformcement Learning

Regression model:

• Linear
• Polynomial
• Random Forest
• when take past in account then RNN, CNN, Transformers

dimensionality reduction: Simplify the data without loosing too much information. Feature extraction: merge one feature in an other and both will represent an new feature.

Anomaly detection: unusual credit card transactions to prevent fraud. Novelty detection: detact new instances that look different from all training instances.

Association rule learning: dig in large datasets and discover interesting reations between attributes.

Types of ML

1. Supervised Learning
   • Classification
   • K-Nearest Neighbours
   • Linear/Logistic regression (both predictor and their labels required)
   • SVM
   • Decision Tree
   • Random Forests
   • Neural Networks
2. Unsupervised Learning
   • Clustering
     • K-Means
     • DBSCAN
     • HCA (Hierarchical Cluster Analysis)
   • Anomaly detection and novelty detection
     • One-class SVM
     • Isolation Forest
   • Visualistation and dimensionality reduction
     • PCS (Principal Component Analysis)
     • Kernel PCA
     • LLE (Locally Linear Embedding)
     • t-SNE (t-Distributed Stochastic Neigbor Embedding)
   • Association rule learning
     • Apriori
     • Eclat
3. Semi-supervised Learning
   • partly labeled
   • mostly combination of supervised and unsupervised learning
   • DBN (Deep Believe Networks)
   • RBM (Resticted Boltzmann Machines)
4. Reinforcement Learning
   • Call an agent, observe the environment, select and perform actions, get reward or penalty. Learns by itself.

System can wheter or not learn increamentally from stream of incomming data

1. Batch Learning
   • must train with all data available (offline learning)
   • can take many hours to train and requires resources
   • when extreme much data, can be impossible to train or limited resources
   • new data then train system from scratch and (automatic) deploy
2. Online learning
   • train system incrementially, by feeding sequential data.
   • either individually or in mini-batches
   • great when data arrives in an continous flow
   • great approach when system needs to adapt quicky to new data.
   • requires less resources
   • when data does not fitt in memory => out-of-core learining then online learning perfect approach.
   • important parameter: learning rate
     • High then adapt quickly to chancing data, but forgets also quickly.
     • Low then learns slower, bet less sensitive to new data.
   • Problem when feed with bad data => performance will decline. Need to monitor system.

ML Systems how they generalize. Needs to prform well on new data!

1. Instance-based learning
   • learn examples by heart, then generalize to new cases by using similarity measures to compare.
2. Model-based learning
   • build model of examples and then use the model to make predictions
   • Use model selection to select an appropiate model and fully specifying its architecture (incl tune parameters)

Inference: make predictions on new data.

Main challenges of ML

"Bad algorithme" and "bad data"

1. Insufficient quantity of training data
   • Not always easy and/or cheap to get extra training data
   • More data is better.
2. Nonrepresentative Training data
   • Crusial the data represents the case to generalize about. For both instant-based en model-based learning
   • If sample too small => sampling noise
   • If large sample but sampling method is flawed => sampling bias Sampling method: how data is collected
3. Poor-Quality data
   • Errors, outliers, noise => clean up data
   • Clearly outliers => discard them of try fix errors
   • missing few features => whether ignore attribute or fill in values manually
4. Irrelevant Features
   • Come up with a good set of features for training => feauture engineering:
     • feature selection (select most useful)
     • feature extraction (combine existing features to more useful one)
     • creating new features by gathering new data
5. Overfitting the training data Overfitting => model performs well on training dat, but does not generalize well. Overfitting happens when model is too complex relative to the amount and noisiness of the training data. Overfitting solutions: - simplify model by selecting model with fewer parameters, reducing number of attributes, constraining model. Constraining model => regularization result: will fit less the training data but generalises better to new data. Amount of reqularization is controlled by hyperparameters. A hyperparameter is a parameter of the learning algorithme (not of model) - gather more training data - reduce the noise in the training data (fix errors, remove outliers)
6. Underfitting the training data
   • Model is too simple to learn the structure of the data.
   • Solutions:
     • select more powerful model, with more parameters
     • improve feature engineering tasks
     • reduce contrains on the model (eg regularisation)

Testing and validation

Training set and test set, relate to each other 80-20% to 99-1%; depending the absolute size total data set. Error rate in test set => generalization error Training error low, but generalization error high => overfitting

Hyperparameter tuning an model section

Holdout validation: keep part of the training set (= validation/development set) to validate several candidata models and select the best. Work mostly very well, except when the validation set is too small. Solution: cross-validation => using many small validation sets and validate each model per validation set.(drawback: trainingtime is multiplied by number of validation sets)

Data Mismatch

Most important rule: validation set and test set must be as representative as possible of the data used in production Training set => to train model Test set => evaluate after happy with the dev set Dev + test set have to come from the SAME distribution (random shuffle data)

1. define dev set + metric. Quickly iterate idea -> code -> experiment

   Set	percentage of data
   training	98
   dev	1
   test	1