Prerequisite: Familiarity with Algorithms, Probability, Linear Algebra, Programming

Course Content

  1. Introduction to the course, revision of linear algebra and probability (3 hours)

  2. Regression: linear regression, ridge regression (3 hours)

  3. Classification: (9 hours)
    • Linear discriminant analysis, logistic regression, perceptrons,
    • support vector machines, Bayes classifier, decision tree.
    • Nonparametric methods: k-nearest neighbours, Parzen window.
  4. Principal component analysis, Canonical correlation analysis (3 hours)

  5. Evaluation and Model Selection: ROC Curves, Evaluation Measures, Cross validation, Significance tests (3 hours)

  6. Ensemble methods: boosting, bagging, random forests (3 hours)

  7. Clustering: (9 hours)
    • k-means, hierarchical, density based clustering
    • Gaussian mixture model
  8. Sequential Learning : hidden Markov model (6 hours)

  9. Neural network : feedforward NN (3 hours)

Learning Outcomes

  • State definitions, theorems/results, algorithms related to key concepts
  • Apply standard techniques to solve known problems
  • Given a task, derive a learning model by defining appropriate loss function, regulariser, optimization problem and stating the best possible solution.
  • Analyse and compare models and algorithms with respect to their complexity, performance and applicability
  • Develop models/algorithms with small modifications of existing standard techniques for a modification of known task

Text Books

  1. Richard Duda, Peter Hart, David Stork, Pattern Classification, 2nd Ed, John Wiley & Sons, 2001. ISBN 9788126511167
  2. Christopher Bishop. ​Pattern Recognition and Machine Learning​. ISBN 0387310738.
  3. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. ISBN 0387952845.

References

  1. Tom Mitchell. Machine Learning. McGraw-Hill. ISBN 0070428077.
  2. Shai Shalev-Shwartz, and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. ISBN 978-1-107-05713-5.

Course Metadata

Item Details
Course Title Machine Learning
Course Code DS3010
Course Credits 3-0-3-5
Course Category PC
Proposing Faculty Sahely Bhadra
Approved on Senate 20 of IIT Palakkad
Course prerequisites Intrtoduction to Optimization Probability and Statistics
Course status NEW
Course revision information Same as CS5512
Course pre-revision code CS5512