Please find below a list of ML methods that you can choose from for your ML project.

Note that in stage 2 of the project you will compare multiple methods, which must include at least 2 methods in different rows of the tables below.

If your desired methods are not in the lists below, please get in touch with us on Slack. We could consider added those into the lists if they are touched upon in the lectures or assignment. 

Course book: mlbook.cs.aalto.fi


Classification methods

Name
 Map Loss Example code package ML book
K-Nearest Neighbors
piece-wise constant around training data points.
0/1 loss KNeighborsClassifier 3.13
Logistic Regression
linear maps Logistic loss LogisticRegression 3.6

Decision Tree or Random Forest


maps represented by a signal flow chart ("tree") that takes in features and maps them to a prediction by executing a series of “ if/else” decisions.

 Gini impurity/   Information gain  3.10
SVC
 linear maps applied to transformed features Hinge loss 3.7, 3.9
Multi-layer perceptron
linear maps + linear/nonlinear activation functions
Logistic loss
3.11
Convolutional neural network (for classification) nonlinear/neural network Various CNNs for image classification with Tensorflow  -


Regression methods

Name
 Map Loss Example code package ML book
Linear Regression or Polynomial Regression
 linear maps
 squared error

LinearRegression (with or without PolynomialFeatures)

3.1
Linear Regression with regularization: Ridge Regression or Lasso Regression
 linear maps Mean squared error loss regularised by L2-norm (Ridge) or squared error plus L1-norm of parameters (Lasso)
Ridge or Lasso
7.1, 7.4, 7.8.1. 3.4
Decision Tree or Random Forest
 maps represented by a signal flow chart ("tree") that takes in features and maps them to a prediction by executing a series of “if/else” decisions.
squared error
 -
Multi-layer Perceptron
 map represented by a signal flow chart or "artificial neural network"
 squared error
3.11

Clustering

Name
 Example code package ML book
k-means
KMeans 8.1
DBSCAN
 DBSCAN 8.3


Feature Learning

Name
 Example code package ML book
Principal component analysis
PCA 9.2

Last modified: Tuesday, 6 September 2022, 5:57 PM