MPhil-PhD Courses Offered by IE Department

MScIE students can opt to take MPhil-PhD courses offered by the Department of Information Engineering. MPhil-PhD courses to be offered in recent years are listed below:

MPhil-PhD Courses Offered 

ENGG5383(IERG5240) Applied Cryptography

This is a graduate-level course on cryptography. It focuses on the definitions and constructions of various cryptographic schemes and protocols, as well as their applications. Useful tools for securing practical systems and emerging techniques in the applied research community will be introduced. No prior knowledge of security, cryptography, or number theory is required.

  • Introduction: a brief history, applications in distributed systems; basic number theory
  • Symmetric-key encryption: definition, information-theoretic security, Entropy, PRNG
  • Provable security: bounded adversary, random oracle model, basic primitives, reduction
  • Public-key encryption: modelling security, Diffie-Hellman protocol, hybrid encryption
  • Authentication: Hash function, collision-resistance, MAC, unforgeability
  • Public-key infrastructure: certificate management, deployment, and revocation issues
  • More schemes: Fiat-Shamir transformation, Cramer-Shoup encryption, identity-/attribute-based encryption, certificateless encryption, proxy re-encryption, broadcast
  • Privacy-enhancing cryptography: zero-knowledge proof, anonymous credentials
  • Pairing-based cryptography: elliptic curve basic, short signature, searchable encryption

IERG6120 Advanced Topics in IE I

This is an introductory course in Compressive Sensing and Sparse Recovery. We intend to cover the following topics:
Introduction to sparse recovery, Compressive sensing via L-1 minimization, Restricted   Isometry Property (RIP), Johnson-Lindenstrauss Lemma, Robustness under L-p norms, Lower bounds, Matching pursuit algorithms, Message passing algorithms, 1-bit compressive  sensing, Group  Testing,  Other  sparse  recovery  problems  such  as Low-rank  matrix  completion,  Nuclear  norm  minimization, and  Sparse  FFT.  This  course  will  partly  follow  a  seminar style   (after   the   basics   have   been   introduced).   Basic knowledge of linear algebra and probability is assumed.

IERG6200 Advanced Topics in Computer Networks

This course will have two parts of contents. Part I is on optimizations in networked systems including wireless networks and cloud systems. Part II is on optimization in energy systems including data centers and power grids.

Part I: Many networked-system design problems are optimization problems in nature. Many of these large-scale problems require distributed solutions that only incur local and simple actions and at the same time attain global optimality. Distributed and stochastic optimization has become a powerful modeling language and solution tool for addressing these design problems.

Part II: A critical challenge faced by today’s energy systems is to optimize the long-term system performance subject to future demand/supply uncertainty? Recently, online optimization and algorithm design have shred new lights on addressing this “new” challenge, and have provided paradigm-shrift solutions for energy systems including data centers and power grids.

In this course, we will study the fundamental of distributed and stochastic optimization, as well as online optimization and algorithm design. We plan to discuss two frameworks in addressing general convex and combinatorial network problems, and two frameworks on online optimization and algorithm design. We will discuss their principles, approaches, and issues involved. To keep the math vibrant, we will demonstrate their applications in various problem domains, ranging from wireless to cloud computing to power systems. We wish to illustrate how the frameworks can be applied to synthesize effective solutions, providing a useful guideline in protocol and system design in practice. In both theory and practical problem domains, we will cover classic results, current research, and open problems.

IERG6300 Theory of Probability

The course covers the following topics: Construction of measures, integration, transformation, product spaces, distributions, expectation, Borel-Cantelli lemmas, characteristic functions, weak convergence, independence, weak law of large numbers, strong law of large numbers, central limit theorem, conditional expectation, Markov chains, stopping times and renewal times, martingales, martingale convergence Theorems, Doob's decomposition theorem, up-crossing inequality, and Birkhoff's ergodic theorem. The focus will be on mathematical rigor and development of all the tools to prove the results formally. Advisory: Students are expected to have basic background in probability and real analysis at undergraduate level.

ENGG5303(IERG5100) Advanced Wireless Communications

This course provides an extensive introduction to basic principles and advanced techniques in the physical layer of wireless communications. Topics to be covered include channel coding, MIMO and space-time processing, OFDM and multicarrier systems, spread spectrum and CDMA, channel capacity, opportunistic scheduling and diversity schemes. Advisory: A prior undergraduate level course in wireless communication is highly recommended.

ENGG5392(IERG5040) Lightwave System Technologies

This course covers the design of advanced optical fiber communication systems. Topics include: optical signal characterization and spectral efficient optical modulation formats, high-speed signal transmission & multiplexing techniques, linear & nonlinear fiber effects and fiber transmission impairments, basic guided-wave optoelectronics and novel integrated optical devices (tunable lasers, planar lightwave circuits, silicon photonics), optical signal amplification, regeneration and performance monitoring techniques, coherent optical communications and enabling digital signal processing techniques, and examples of optical subsystems for optical networks. Advisory: Students are expected to have basic background in optical communications.

IERG5200 Channel Coding and Modulation

This course covers classic and new channel coding, and related modulation schemes. Topics include Reed-Solomon code, convolutional code, concatenation, low-density parity-check (LDPC) code, and optionally, OFDM, MIMO, and network coding.

IERG6130 Advanced Topics in IE II

This is a graduate-level introduction to machine learning and probabilistic inference. Instead of teaching classical techniques like SVM and HMM, this course aims to provide students with principled perspectives on machine learning methodologies and contemporary approaches to building statistical models and algorithms. Our ultimate goal is that students, after taking this course, will be able to formulate a new model given a practical problem and choose appropriate algorithms to solve the problem. Machine learning is a subject that relies heavily on mathematical and statistical analysis. Students who take this course should have good understanding of linear algebra and elementary probabilistic theory. Topics: Exponential family distributions and conjugate prior, Monte Carlo Methods, Markov Chain Monte Carlo Generalized linear model, Empirical risk minimization and Stochastic gradient descent, Proximal methods for optimization, Graphical models: Bayesian Networks and Markov random fields, Sum-product and max-product algorithms, Belief propagation, Variational inference methods, Gaussian Processes and Copula Processes Handling Big Data: Streaming process and Core sets.

IERG 6210 Advanced Topics in Information Processing

In this course, we will cover some advance research topics in the following areas: Face recognition, 3D line drawing reconstruction, video surveillance, and photo quality assessment. Students are required to conduct a research project, give an one hour research presentation, and submit a final research report.