**Instructor: **

**Shayan Srinivasa Garani,**

**Course Syllabus: **

**Introduction to neural networks:**Neuron models motivated by biology, feedback and other network architectures, Knowledge representation and artificial intelligence.**Learning Process:**memory-based learning, error-correction based learning, Hebbian learning, competition learning, Boltzmann Learning, Supervised and unsupervised learning methods, memory and adaptation, Statistical nature of the learning processes.**Multilayer Perceptron:**Perceptron, Perceptron convergence theorem, back propagation algorithm and Applications, XOR problem, functional approximation and curse of dimensionality.**Radial Basis Function networks:**Cover’s Theorem for separability of Patterns, regularization theory and networks, approximation properties of RBFs, kernel regression and Learning strategies, applications.**Support Vector Machines:**Optimal hyperplane for linear separable patterns and non- separable patterns, SVMS or pattern recognition, epsilon-insensitive loss function, SVMs or nonlinear regression, applications.**Committee Machines:**Associative Gaussian mixture model, hierarchical mixture of experts (HME) model, EM algorithm, Application of EM algorithm to HME.**Principal Component Analysis:**Eigen structure of PCA, Hebbian based maximum Eigen filter, Hebbian based PCA adaptive PCA using lateral inhibitions (APEX), PCA based on neural networks: reestimation and decorrelating algorithms, Kernel PCA applications.

**Reference Books:**

- Neural Networks: A Comprehensive foundation by Simon Haykin, Prentice Hall of India. (required)
- Machine Learning: Probabilistic Perspective. Kevin Murphy, MIT Press. (optional)
- Class notes

**Grading Policy:**

Homeworks: 50%

Project: 25%

Final exam: 25%

**Homeworks:**

- Homework #1 Solution #1
- Homework #2 Solution #2
- Homework #3 Solution #3
- Homework #4 Solution #4
- Homework #5 Solution #5

**Exams:**

**Project:**

**Course announcements**

**Project report due date: 7th May 2017.****Project presentations and demos : 9th May 2017.****Final exam: 11th May 2017.**