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Data Security
Complete theory for the Data Security exam: information theory, source coding, error detection and correction codes, cryptography, digital electronics, semiconductor memories and social engineering.
Statistics & Data Science
Statistical foundations for AI/ML: descriptive and inferential statistics, hypothesis testing, probability theory, digital health psychometry, and advanced mathematical concepts for data science.
Machine Learning & Deep Learning
Core ML/DL concepts: supervised and unsupervised learning, neural networks (CNN, RNN/LSTM, GAN, Transformers), training techniques, model optimization, and real-world applications in software engineering and NLP.
AI Foundations & Philosophy
Epistemology and scientific method, reasoning types (deductive, inductive, abductive), learning theories, Industry 4.0 and Big Data concepts, AI ethics and socio-political implications, determinism vs freedom.
Programare Python
Programare Python: variabile, tipuri de date, structuri de control, funcții, structuri de date (liste, dicționare, tupluri, mulțimi), OOP și pregătire examen.
Prompt Engineering
Tehnici și principii pentru elaborarea prompt-urilor eficiente în interacțiunea cu modele de limbaj (LLM). Acoperă definiții fundamentale, greșeli frecvente și tehnici avansate precum Persona Pattern, Chain-of-Thought și Few Shots.
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