BTECH IN ELECTRONICS AND COMMUNICATION ENGINEERINGelectivetheory
MACHINE LEARNING
ECE 4409
Syllabus
- 01Machine learning basics
- 02Naïve Bayesian Model
- 03Non-Parametric Techniques: Density Estimation
- 04Parzen Windows
- 05k- Nearest-Neighbor Estimation
- 06K- nearest neighbor classification
- 07Radial Basis Function Network
- 08Learning Vector Quantization
- 09Clustering
- 10K-Means clustering
- 11Competitive learning
- 12Support vector machines
- 13feature selection methods – Filter based techniques and wrapper methods
- 14Principal Component Analysis
- 15Applications of PCA
- 16PCA
- 17Independent component analysis
- 18Voting
- 19Error correcting output codes
- 20Bagging
- 21Boosting
- 22Self directed learning: Self-Organizing Maps
- 23Recurrent Neural Network
- 24Hopfield Neural Network
- 25Adaptive Resonance Theory
- 26Statistical Hypothesis testing- t-test
- 27ANOVA
References
- Alpaydin E, “Introduction to Machine Learning”, (2e), MIT Press, 2010
- Duda R.O, Hart P.E. and Stork D.G., “Pattern Classification”, (2e), Wiley, 2001
- Harrington P., “Machine Learning in Action, Manning” Publications, 2012
- Bishop C. M., “Pattern Recognition and Machine Learning”, Springer, 2007
- Jensen R. and Shen Q. “Computational Intelligence and Feature Selection”: Rough and Fuzzy Approaches, Vol. 8, IEEE Press Series on Computational Intelligence, John Wiley and Sons, 2008
Credits Structure
3Lecture
0Tutorial
0Practical
3Total