AI in Networks

Course Curriculum

1. Evolution of networks: Introduction to data, data-driven intelligence; evolution of networks: Introduction to networks: circuit switching, packet switching, TCP/ IP, VOIP, network layers; need for AI in Networks; Practical applications of AI at different network layers; Understanding cross layer network design (concepts of user Quality of Service, routing, scheduling). 

2. Statistical learning:  Basics of probability and random variables, understanding datasets, KL-divergence minimization and empirical loss minimization, Parametric modeling, Bayes classifier, ML-MAP estimator, bias-variance tradeoffs, linear models; Non-parametric modeling; Optimization and need for optimization in machine learning. 

3. Parameter optimization (Convex): Concepts of covex sets, functions, L-smoothness, μ strongly convex functions; Hierarchy of convex optimizations: Linear programming, quadratic programming, second order conic programming, semi-definite programming;  Lagrangian function, properties of Lagrangian, Dual function, and dual problem formulation, Weak duality, Strong duality; Slaters condition for guaranteeing strong duality, KKT conditions; Gradient descent, stochastic gradient descent algorithms; 2nd order descent methods   

4. Non-convex optimization algorithms: Approximate gradient and hessian algorithm, Successive approximation methods (linear and quadratic approx.), Coordinate descent methods (block coordinate descent and block successive upper bound minimization), Branch and bound method; Heuristic algorithms: Simulated annealing, Genetic algorithms, and Ant Colony Optimization 

5. Use cases of AI in wireless networks: Basics of wireless networks, introduction to the wireless channel, AWGN noise, fading noise; Applications of AI:  Wireless channel estimation and prediction in 5G+ networks, Research studies on Load-Energy Balancing in Wireless networks, Scheduling and Routing in Wireless networks, Receiver design and understanding SNR and bit error rate in BPSK, QPSK systems. 

6. Use cases of AI in object estimation, tracking, localization: Parameter estimation using discrete time Kalman filter, practical examples on object tracking, object localization in wireless networks. 

Note: Programming assignments/ hands on project will be given for modules 2-6.

Grading 

Assignments/ Quiz: 40%

Programming exercises: 30% 

Final exam: 30%