Department of Mathematicselectivetheory
FUNDAMENTALS OF MACHINE LEARNING
DSE 2222
Syllabus
- 01Machine Learning Basics: Types of Machine Learning, Supervised vs. Unsupervised Learning, Parametric vs. non-parametric models, Instance Based learning–k-nearest neighbors, Simple Regression Models: Linear, Logistic, Cost functions, Gradient Descent, Batch Gradient Descent, Over fitting, Model Selection, No free lunch theorem, bias/variance trade-off, union and Chernoff bounds, VC dimensions
- 02Bayesian Models: Bayesian concept learning, Bayesian Decision Theory, Naïve Bayesian, Laplacian Correction, Bayesian Belief Networks
- 03Tree Models: information theory, decision tree induction, tuning tree size, ID3,C4.5,CHAID, Decision Stump
- 04Support Vector Machines: kernel functions, Regression Models: Ridge and Lasso Regression, GLM and the exponential Family
- 05Bagging algorithm, Random Forests, Grid search and randomized grid search, Partial dependence plots
- 06Ensembling and Boosting Algorithms: Concept of weak learners, Adaptive Boosting, Extreme Gradient Boosting (XGBoost)
- 07Artificial Neural Networks: Perceptron, Backpropagation, Hopfield Network
- 08Curse of Dimensionality: Factor Analysis, Principal Component Analysis(PCA), Difference between PCAs and Latent Factors
References
- K.Murphy, Machine Learning: A Probabilistic Perspective, MIT Press,2012
- G. James, D. Witten, T Hastie, R Tibshirani, An introduction to statistical learning with applications in R, Springer, 2013
- J. Han, M. Kamber, J. Pei, Data Mining concepts and techniques, (2e), Morgan Kaufmann-Elsevier,2011
- T.Hastie, R.Tibshirani, J.Friedman, The Elements of Statistical Learning, (2e),Springer,2009
- T.M.Mitchell, Machine Learning,(Indian Edition),MacGrawHill,2017
- C.Bishop,Neural Networks for Pattern Recognition, Oxford University Press,2019
Credits Structure
3Lecture
0Tutorial
0Practical
3Total