C1 The Machine Learning Landscap

Application: OCR, spam filter

To summarize, Machine Learning is great for:
• Problems for which existing solutions require a lot of hand-tuning or long lists of
rules: one Machine Learning algorithm can often simplify code and perform bet‐
ter.
• Complex problems for which there is no good solution at all using a traditional
approach: the best Machine Learning techniques can find a solution.
• Fluctuating environments: a Machine Learning system can adapt to new data.
• Getting insights about complex problems and large amounts of data.

Supervised:
KNN
Linear Regression
Logistic Regression
SVM
Decision Trees and Random Forests
Neural Networks

Unsupervised:
Clustering:
k-means
Hierarchical Cluster Analysis
Expectation Maximization
Visualization and dimensionality reduction
Principal Component Analysis
Kernel PCA
Locally-Linear Embedding
t-distributed Stochastic Neighbor Embedding (t-SNE)
Association rule learning
Apriori
Eclat

Feature Extraction
Anomaly detection

Deep belief networks
Restricted Boltzmann machines

Batch vs Online Learning (mini-batch)

Main Challenges of Machine Learning
Insufficient Quantity of Training Data
Nonrepresentative Training Data
Poor-Quality Data
Irrelevant Features

Overfitting the Training Data
(Overfitting happens when the model is too complex relative to the
amount and noisiness of the training data. ) -> degrees of freedom

Lastly, your model needs to be neither too simple (in which case it will
underfit) nor too complex (in which case it will overfit).

cross-validation