MCA E3011
Machine Learning
Notes are on their way.
For now, use the syllabus outline to see what this subject covers.
Syllabus topics
Machine Learning – what and why? Basics of Linear Algebra and Statistics, Overview of target function representations; Linear Regression. [Q-1]
Notes will be added here.
Syllabus: supplied PDF, page 32.
Syllabus topics
Basics of Feature Selection and Evaluation, Decision Tree, Overfitting and Pruning, Logistic regression, Support Vector Machine and Kernel; Noise, bias-variance trade-off, under-fitting and over-fitting concepts. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 32.
Syllabus topics
Perceptions: representational limitation and gradient descent training. Multilayer networks and back propagation. Hidden layers and constructing intermediate, distributed representations. Overfitting, learning network structure, recurrent networks. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 32.
Syllabus topics
Learning from unclassified data. Clustering. Hierarchical Agglomerative Clustering. K-means partitional clustering. Expectation maximization (EM) for soft clustering. Semi-supervised learning with EM using labelled and unlabeled data. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 32.
Syllabus topics
Committees of multiple hypotheses, bagging, boosting, active learning with ensembles. [Q-1]
Notes will be added here.
Syllabus: supplied PDF, page 32.
Check your university’s syllabus for the topics covered in your exams.