Course Details
Subject {L-T-P / C} : EC3606 : Introduction to Machine Intelligence { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Samit Ari
Syllabus
Overview of Neural Network: Introduction to Artificial Neural Networks (ANN), Models of a Neuron, Network structure Error–correction learning, Feed-forward Network Functions, Single neuron/ Perceptron networks: Network Training, Gradient descent optimization, Multilayer Perceptron: Back propagation algorithm. Introduction to Machine Learning: Types of machine learning, Supervised learning, Unsupervised learning, basic concepts in machine learning, K Nearest Neighbors. Kernels and SVM: Kernel functions, Optimal Hyperplane for linearly patterns, Optimal Hyperplane for non-separable patterns, SVMs for classification. Dimensionality Reduction: Subset Selection, Principal Component Analysis (PCA), linear discriminant analysis (LDA). Introduction to Deep learning: Introduction to Neural Networks, Deep generative models, Deep directed networks, Deep belief networks, Deep neural networks, Deep auto-encoders, Applications of deep networks.
Course Objectives
- To understand the machine learning algorithms.
- To design and development of the machine learning algorithms for solution of different problems.
Course Outcomes
Students will have the understanding about different machine learning techniques and they will learn the procedure to apply these techniques to solve the real time problems.
Essential Reading
- S. Haykin, Neural Networks - A Comprehensive Foundation, Peasrson Education, India
- C. M. Bishop, Pattern Recognition and Machine Learning, Springer , 2013
Supplementary Reading
- Tom M. Mitchell, Machine Learning, McGraw Hill Education (India) , 2013
- Ethem Alpaydin, Introduction to Machine Learning, MIT Press , 2nd edition, 2010.