anamaria1708 / college-labs Goto Github PK

During my graduate studies at the University of Zagreb, Faculty of Electrical Engineering and Computing, I've had the pleasure of documenting, testing and implementing a plethora of algorithms. The solutions are grouped by course and each one has its own folder. Additional information about the course or exercise can be found in its respective folders.

• Solving systems of linear equations - matrix manipulation, forward and back substitution, LU and LUP decomposition
• Single variable nonlinear function optimization - golden section search, Nealder-Mead simplex method, Hooke-Jeeves method
• Multiple variable nonlinear function optimization - gradient descent, Newton-Raphson method
• Constrained optimization - Box method, constrained problem transformation
• Genetic algorithms - representation, crossover, mutation, optimal parameter search
• Numerical integration - Runge-Kutta methods

• Propositional logic, predicate logic, Computation Tree Logic (CTL), Kripke structure
• NuSVM language and programming
• Linear Temporal Logic (LTL), fixed point theory
• JavaPathFinder tool and programming
• Hardware Description Language (VHDL), Verilog
• Promela language and Spin tool
• Binary Decision Tree (BDD), Reduced Order BDD (ROBDD), ITE algorithm
• DPLL, GRASP and Chaff methods

• Communication between processes - message queues, pipelines
• Mutual exclusion in distributed systems - Lamport's distributed protocol, Ricart-Agrawala protocol
• Cryptography - security demands, DES, AES, block ciphers, RSA, Miller-Rabin primality test, digital envelope, signature and seal, hash functions
• Security protocols - Diffie-Hellman protocol, Needham–Schroeder protocol, Kerberos protocols, PKI
• RAID

NOS keys: DES is Data Encryption Standard, AES is Advanced Encryption Standard, RSA is Rivest–Shamir–Adleman, PKI is Public Key Infrastructure, RAID is Redundant Array of Inexpensive Disks.

• Introduction to High Performance Computing (HPC) - parallel programming models, properties and sequential to parallel conversion
• Message Passing Interface (MPI) - process communication, MPI functions, blocking and non-blocking
• Parallel Random Access Machine (PRAM) - synchronous and asynchronous, *_reduce algorithm
• Designing parallel programs - phases, hypercube algorithm, N-tree algorithm
• Quantitative analysis of parallel algorithms - time to execute, efficiency and isoefficiency, speedup, scalability

• Linear regression, cross validation, feature, model and hyperparameter analysis
• Logistic regression, one-vs-one, one-vs-rest classification
• Support vector machines, kernel functions, K nearest neighbours
• Ensambles, maximum likelihood and aposteriori estimators
• Probabilistic graphical models, clustering