EC346 Foundations of Machine Learning

Course Name: 

EC346 Foundations of Machine Learning

Programme: 

B.Tech (ECE)

Category: 

Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-1-0) 4

Content: 

Statistical foundations, Different Paradigms of Pattern Recognition, Probability estimation, Proximity measures, Feature extraction, Feature extraction, Different approaches to Feature selection, Nearest Neighbour Classifier and variants, Efficient implementations, Prototype selection. Bayes classification. Linear models, regression, logistic regression, neural networks, objective function and learning, backpropagation. Kernel based methods, support vector machines. Dimensionality reduction, principal component analysis, reconstruction, discriminant analysis. Clustering, K-means algorithm, distance measure, objective function, initialization. Anomaly detection, recommender systems. Scaling of algorithms

References: 

R. O. Duda, P. E. Hart and D. G. Stork Pattern Classification, Wiley Publications, 2001
D. McKay Information Theory, Inference, and Learning Algorithms, Cambridge University Press 2003
C. M. Bishop Pattern Recognition and Machine Learning, Springer, 2006

Department: 

Electronics and Communication Engineering(ECE)
 

Contact us

Dr. T. Laxminidhi,  Professor and Head, 
Department of E&C, NITK, Surathkal
P. O. Srinivasnagar,
Mangalore - 575 025 Karnataka, India.

Connect with us

We're on Social Networks. Follow us & get in touch.