Technical Transcript

Study of fundamental data structures including arrays, linked lists, stacks, queues, trees, and graphs, with emphasis on implementation and applications.

Fundamental concepts of calculus including limits, derivatives, integrals, and their applications in science and engineering.

Advanced calculus topics including multivariable calculus, partial derivatives, multiple integrals, and vector calculus.

Study of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues, and eigenvectors.

Introduction to probability theory, random variables, probability distributions, hypothesis testing, and regression analysis.

Introduction to machine learning algorithms including supervised learning, unsupervised learning, and reinforcement learning techniques.

Foundational concepts of AI including search algorithms, knowledge representation, reasoning, and planning.

Overview of AI concepts, techniques, and applications, including machine learning, natural language processing, and computer vision.

Study of generative AI models, their principles, influences, and applications in various fields.

Advanced techniques for applying machine learning to data analysis problems, including feature engineering, model selection, and evaluation.

Application of AI techniques in autonomous vehicles, including computer vision, sensor fusion, and decision-making systems.

Study of database design, SQL, relational algebra, database normalization, transaction management, and database security.

Introduction to big data technologies and their applications in agriculture, including data collection, processing, and analysis.

Application of information technology in agriculture, including precision agriculture, agricultural data analysis, and decision support systems.

Techniques for analyzing large-scale vehicle data, including data collection, storage, processing, and visualization for intelligent transportation systems.

Study of big data infrastructure components, including distributed file systems, cluster computing frameworks, and cloud computing platforms.

Techniques and practices for annotating various types of data (text, images, audio, video) for machine learning and AI applications.

Fundamental concepts of computer networks, including network architectures, protocols, TCP/IP model, routing, and network security.