Artificial Intelligence (in English)

The course is designed to provide a comprehensive introduction to the field of artificial intelligence. The course will cover the fundamental concepts and techniques in AI, including problem-solving, knowledge representation, reasoning, machine learning, and intelligent systems. The course will include hands-on programming assignments and projects to help students develop practical skills in AI.

Topics:

1. Introduction to AI: History, Ethical Issues, Mathematical prerequisites.
2. Acquiring and Preparing the Data. Data preprocessing.
3. Machine learning - Supervised learning, Unsupervised learning, Reinforcement learning.
4. Decision trees, random forests.
5. Neural networks: Perceptron model, feed-forward neural networks, Multi-Layer layer neural networks, Backpropagation Algorithm.
6. Types of ANNs: CNNs, RNNs, LSTM, GRU, Transformers, BERT, GPT Series, Siamese Networks, CapsNets, Autoencoders, GANs, Attention-Based Models, GNNs, Neural Style Transfer Networks, and Neuro evolution.
7. Intelligent Systems: Support Vector Machines, K-mean.
8. Knowledge representation and reasoning in rule-based systems: Uncertainty management in rule-based systems.
9. Problem solving as search: Problem spaces, Uninformed search, BFS, DFS, Limited DFS, Iterative deepening search, Informed search, Heuristic search, Best-first search, Greedy, A* algorithm, A* variants.
10. Local search: Simulated annealing, Hill climbing. Evolutionary computation: Evolutionary algorithms.
11. Evolutionary Computation: Evolutionary strategies, Evolutionary programming, and Genetic programming.
12. Swarm intelligence: Particle swarm optimization, Ant colony optimization.
13. Adversarial Searching: Gameplaying, Minimax search, Alpha-beta pruning.

Computer Graphics (in Romanian)

Cursul este conceput pentru a oferi o introducere cuprinzătoare în domeniul graficii pe computer. Cursul va acoperi conceptele și tehnicile fundamentale din grafică pe calulator și va include sarcini practice de programare și proiecte.

Topics:

1. Bazele programării în grafica interactivă. Hardware pentru grafică.
2. Transformări geometrice uzuale în grafica 2D şi 3D. Sisteme de coordonate. Vizualizarea 3D.
3. Limbajul GLSL (OpenGL Shading Language).
4. Modalităti de transmitere a informaţiilor către shadere. Modelarea suprafeţelor şi curbelor.
5. Modelarea obiectelor (solide) 3D. Modelare geometrică şi ierarhii.
6. Modelarea luminii – modele locale.
7. Modelarea luminii – modele globale.
8. Modelarea luminii – umbre, reflexii, refractii.
9. Texturi (constante, variabile şi aleatoare).
10. Crearea și încarcarea Modelelor 3D.
11. Realizarea animaţiei.
12. Evaluarea și optimizarea aplicaţiilor grafice.

Quantum Computing (in English)

The course is designed to provide a comprehensive introduction to the field of quantum computing. The course will cover the fundamental concepts and techniques in quantum computing, including quantum gates, quantum circuits, quantum algorithms, and quantum programming. The course will include hands-on programming assignments and projects to help students develop practical skills in quantum computing.

Topics:

1. Introduction to Quantum Computing: History, Ethical Issues, Mathematical prerequisites.
2. Introduction – mathematical prerequisites
3. Fundamental notions of quantum computing (Qubits and the Bloch Sphere).
4. Qubit gates. Quantum circuits.
5. The quantum Fourier transform and its applications, Quantum phase estimation, Schor’s algorithm.
6. Quantum Algorithms: Deutsch-Jozsa algorithm, Simon's algorithm, Grover's algorithm.
7. Quantum cryptography and post quantum cryptography. Quantum Computing Attacks on RSA.
8. Quantum key distribution (QKD). Noise in QKD (eye dropper).
9. Clustering Structure and Quantum Computing.
10. Quantum Pattern Recognition. Quantum Classification. Quantum Regression.
11. Physical implementation of quantum systems.
12. Quantum information theory.