Overview of Computer Security Techniques, Conventional Encryption, Public-Key Cryptography, Key Management, Message Authentication, Hash Functions and Algorithms, Digital Signatures, Authentication Protocols, Access Control Mechanisms, Network Security Practice, TCP/IP Security, Web Security, SSL (Secure Socket Layer), Denial-of-Service Attacks, Intrusion Detection, Viruses.

Introduction to cryptographic concepts. Symmetric encryption, the public-key breakthrough, one-way functions, hash functions, random numbers, digital signatures, zero-knowledge proofs, modern cryptographic protocols, multi-party computation. Everyday use examples including online commerce, BitTorrent peer-to-peer file sharing, and hacking some old encryption schemes.

Foundational topics necessary for cyber security, such as basics of programming, computer architecture, operating systems, computer networks, and databases.

Introductory cyber forensics and digital forensics definitions, evidence collection methodologies, data recovery tools, software and hardware tools employed for forensic analysis, evidence reporting procedures and techniques.

Blockchain, distributed consensus, distributed databases, flooding and broadcasting, crypto currencies, security of crypto currencies, blockchain applications, alternative blockchain and crypto currency proposals, smart contracts.

A broad introduction to machine learning covering regression, classification, clustering, and dimensionality reduction methods; supervised and unsupervised models; linear and nonlinear models; parametric and nonparametric models; combinations of multiple models; comparisons of multiple models and model selection.

Introductory penetration testing definitions, white hat attacking methodologies, network and software scanning and inventory tools, exploit tools, social engineering techniques, applied penetration testing software.

Network security, Internet and World-Wide Web security, TLS/SSL, firewalls, intrusion detection and prevention systems, security of various Internet and cloud protocols, virtual machine security.

Cloud computing fundamentals taught using Amazon Web Services (AWS), case studies and tools. Software, platforms, and infrastructure as services. Scalability and elasticity provided by virtualized computing, storage, and network resources. Key cloud security concepts and technologies. Network, application, and information security. Moving existing IT infrastructure to the cloud. Cloud storage. Data analytics on the cloud case studies.

Overview of computer systems security techniques, operating systems security, authentication and access control mechanisms, malware, computer viruses, mobile device security, other modern security topics such as physical security, GSM security, RFID security, financial security.

Legal aspects of cyber security, cyber crime, various data privacy regulations (KVKK, GDPR, HIPAA), electronic signature law, comparative legal analysis of national and international cyber security law.

Secure coding principles, software testing methodologies, techniques and tools for secure software coding, operating system, and database support for secure software, reverse engineering, techniques for hiding code and data.

An introduction to interactive Python and Jupyter Notebooks, Python built-in data structures, conditional statements, loops, functions, strings and basic input/output, basics of data manipulation and visualization with relevant Python libraries, different types of plots, vector/matrix representations, linear algebra operations, probability/statistics operations, data analysis applications.

Introduction to system dynamics and systems thinking; theory and applications to support strategic decision making. Current topics in health policy and management, mapping tools for system dynamics, crisis/pandemic management, case studies, sustainability and management simulations. Concepts of systems thinking and modeling for better decision making and analysis.

Acquisition of technologies for transforming to digital business to stay competitive. Even more critical due to the pandemic. Provides tools to address digital era challenges. Technology road mapping for a comprehensive plan towards digital transformation.

What is big data. Value creation with big data, Data sources and extraction from unstructured sources. Learning tasks and statistical learning. Fundamental concepts and their operationalization; overfitting vs. generalization; curse of dimensionality; correlation vs causation; data collection strategy and biases; security, privacy and ethical considerations.

Principles of data communications and computer networks; ISO/OSI reference model with an emphasis on data links, network and transport layers; TCP/IP protocol suite; asynchronous and synchronous transmission; data link control; multiplexing; wide-area networks; routing; congestion control; local area networks; communications architecture and transport protocols; distributed applications.

Entropy, Relative Entropy and Mutual Information; Asymptotic Equipartition Theory; Entropy Rates of a Stochastic Process; Data Compression; Kolmogorov Complexity; Channel Capacity; Differential Entropy; The Gaussian Channel; Maximum Entropy and Spectral Estimation; Rate-Distortion Theory, Network Information Theory.

Introduction to distributed computing, an overview of operating systems, process synchronization and deadlocks, threads and thread synchronization, communication protocols, synchronization in distributed systems, management of time, causality, logical clocks, consistent global states, distributed mutual exclusion, distributed deadlock detection, election algorithms, agreement protocols, consensus, multicast communication, distributed transactions, replication, shared-memory model, scheduling, distributed file systems, fault tolerance in distributed systems, distributed real-time systems.

A broad introduction to machine learning covering regression, classification, clustering, and dimensionality reduction methods; supervised and unsupervised models; linear and nonlinear models; parametric and nonparametric models; combinations of multiple models; comparisons of multiple models and model selection.

Basic linear models for classification and regression; stochastic gradient descent (backpropagation) learning; multi-layer perceptrons, convolutional neural networks, and recurrent neural networks; recent advances in the field; practical examples from machine translation, computer vision; practical experience in programming, training, evaluating and benchmarking deep learning models.

Advanced topics in data structures, algorithms, and computational complexity. Asymptotic complexity measures. Graph representations, topological order, and algorithms. Forests and trees. Minimum spanning trees. Bipartite matching. Union-find data structure. Heaps. Hashing. Amortized complexity analysis. Randomized algorithms. Introduction to NP-completeness and approximation algorithms. The shortest path methods. Network flow problems.

Tools and techniques for ensuring software reliability. Specification formalisms and languages. Modeling tools and languages. Unit and integration testing. Automated testing and verification tools and algorithms. Mathematical representations for programs and executions. Hoare logic. A specification using modular contracts: Preconditions, postconditions, loop, and object invariants. Ownership systems. Automated test generation. Model-based testing. Coverage metrics for testing adequacy. Type and effect systems for reliable software. Software model checkers. Static analysis. Concurrent/multi-threaded programs. Correctness criteria for concurrent programs: race-freedom, atomicity, linearizability, and serializability. Testing, verification, and debugging tools for concurrent programs.

Discrete and continuous random variables and processes, functions of random variables, independence of random variables. Central Limit Theorem. Discrete-time random processes, continuous-time random processes, stationary random processes, ergodicity, auto and cross-correlation functions, power spectral density; spectral estimation, white noise processes, Markov chains.

The cellular concept, channel assignment strategies, frequency reuse, handoff strategies, interference sources, mobile radio propagation, large-scale path loss, small-scale fading and multipath, modulation techniques for mobile radio, diversity combining, transmit and receive antennas for wireless communication systems, multiple access techniques in wireless, wireless system design for delay intolerant services, wireless system design for delay-tolerant services, error correction coding, and ARQ schemes, wireless networking, wireless systems & standards: GSM, IS-95, cdma2000, W-CDMA, 3GPP2 1xEV-DO, 3GPP2 1xEV-DV, fourth-generation wireless system proposals. Design-oriented exercises using computer aids.

Wireless network applications, wireless channel and communication fundamentals, medium access control protocol, routing protocol, topology control, time synchronization, data-centric networking, wireless communication standards.

Method of descent, unique factorization, basic algebraic number theory, diophantine equations, elliptic equations, p-adic numbers, Riemann zeta function, elliptic curves, modular forms, zeta and L-functions, ABC-conjecture, heights, class numbers for quadratic fields, a sketch of Wiles? proof.

An introduction to measure theory, Kolmogorov axioms, independence, random variables, product measures, and joint probability, distribution laws, expectation, modes of convergence for sequences of random variables, moments of a random variable, generating functions, characteristic functions, distribution laws, conditional expectations, strong and weak law of large numbers, convergence theorems for probability measures, central limit theorems.

Convex analysis, optimality conditions, linear programming model formulation, simplex method, duality, dual simplex method, sensitivity analysis; assignment, transportation, and transshipment problems.

The basic theory of the Poisson process, renewal processes, Markov chains in discrete and continuous time, as well as Brownian motion and random walks are developed. Applications of these stochastic processes are emphasized by examples, which are drawn from inventory and queueing theory, reliability and replacement theory, finance, population dynamics, and other biological models.

Tools, techniques, and skills needed to analyze decision-making problems characterized by uncertainty, risk, and conflicting objectives. Methods for structuring and modeling decision problems and applications to problems in a variety of managerial decision-making contexts. Structuring decision problems: Decision trees, model building, solution methods and sensitivity analysis; Bayes’ rule, the value of information, and using decision analysis software. Uncertainty and its measurement: Probability assessment. Utility Theory: Risk attitudes, single- and multiattribute utility theory, and risk management. Decision making with multiple objectives.

Theory and practice of dynamic programming, sequential decision making over time; the optimal value function and Bellman’s functional equation for finite and infinite horizon problems; Introduction of solution techniques: policy iteration, value iteration, and linear programming; General stochastic formulations, Markov decision processes; application of dynamic programming to network flow, resource allocation, inventory control, equipment replacement, scheduling and queueing control.

Constructive heuristics; improving heuristics; metaheuristics: simulated annealing, genetic algorithms, tabu search, scatter search, path relinking, ant colony

Markovian queues: M/M/1, M/M/C, M/M/C/K systems, and applications. Phase-type distributions and matrix-geometric methods: PH/PH/1 systems. Queueing networks: reversibility and product-form solutions. General arrival or service time distributions: embedded Markov Chains, M/G/1, and G/M/c queues, G/G/1 queues, and the Lindley recursion, approximations. Stochastic comparisons of queues: stochastic orders, sample path properties.

Basic concepts and definitions of system reliability. Series, parallel, k-out-of n systems. Structure functions, coherent systems, min-path, and min-cut representations. System reliability assessment and computing reliability bounds. Parametric families of distributions, classes of life distributions, and their properties. Shock and wear models. Maintenance, replacement, and repair models. Current issues on stochastic modelling of hardware and software reliability.