### Overviews

Introduction to machine learning for

**engineers**
Introduction to machine learning for

**managers**### Applications

Regression Problems, e.g. predict values.

Classification Problems, e.g. predicting class(es).

Clustering, e.g. finding groups in the data.

Recommender Systems, e.g. what products is the specific customer likely to buy.

Anomali Detection, e.g. intrusion detection or fraud detection.

### Tree Based Algorithms

Classification and Regression Tree, CART

Random Forest

Boosted Trees

Gradient Boosted Trees

### Neural Network Based Algorithms

Multilayered Perceptron, MLP, aka Feed Forward Neural Network, FFNN

Convolutional Neural Networks, CNN

Deep Neural Networks, DNN

Recurrent Neural Networks, RNN

Support Vector Machines, SVM

### Various Topics

Dimensionality Reduction

Hyperparameter Optimization