UNIT 1:
Learning models –geometric models –probabilistic models –logic models
Grouping and grading- learning versus design
Theory of learning –feasibility of learning
Theory of generalization –generalization bound
Bias and variance –learning curve
Types of learning –supervised –unsupervised –reinforcement
Bias and variance –learning curve
UNIT 2:
Support vector machines- Soft margin SVM
Learning neural networks structures
Linear classification –univariate linear regression
Perceptrons-Multilayer neural networks
Generalization and overfitting
Generalization and overfitting
UNIT 3:
K-means – clustering around medoids- Silhouttes
Non-parametric regression
k-d trees –locality sensitive hashing–
K-means – clustering around medoids- Silhouttes
k-d trees –locality sensitive hashing–
K-means – clustering around medoids- Silhouttes
UNIT 4:
First-order rule learning
Ranking and probability estimation
Decision trees –learning decision trees
Learning ordered rule lists
UNIT 5:
Support vector regression
Support vector regression
