Course Overview

Machine Learning Course Details:

Class overview: Class organization, topics overview, software etc.

• what is ML
• Problems
• data
• tools
• Visualization

Linear regression :

• SSE
• gradient descent
• closed form
• normal equations
• features
• Overfitting and complexity
• training, validation, test data
• Classification problems
• decision boundaries
• nearest neighbour methods
• Probability and classification
• Bayes optimal decisions
• Naive Bayes
• Gaussian class-conditional distribution
• Linear classifiers
• Bayes’ Rule
• Naive Bayes Model
• Logistic regression
• online gradient descent
• Neural Networks
• Decision tree
• Ensemble methods
• Bagging
• random forests
• boosting
• A more detailed discussion on Decision Tree and Boosting
• Unsupervised learning
• clustering
• k-means
• hierarchical agglomeration
• Advanced discussion on clustering and EM
• Latent space methods
• PCA.
• Text representations
• naive Bayes
• multinomial models
• clustering
• latent space models