|
Knowledge and Understanding
|
|
K1
|
Demonstrate a comprehensive understanding of the core concepts, theories, statistical methods, and algorithms underlying artificial intelligence, including machine learning, neural networks, natural language processing, and computer vision.
|
|
K2
|
Acquire knowledge and proficiency in using various AI techniques and tools, such as data preprocessing, feature selection, model selection, and evaluation methods, to develop AI solutions and analyze their performance.
|
|
K3
|
Demonstrate an understanding of the ethical considerations and challenges associated with AI technologies, including privacy, fairness, transparency, bias mitigation, and societal impact, while applying responsible AI practices in real-world scenarios.
|
|
Skills
|
|
S1
|
Develop the technical skills required to design, implement, and evaluate AI models and systems, including proficiency in programming languages (such as Python), data manipulation, algorithm implementation, and utilization of AI libraries and framework
|
|
S2
|
Cultivate the ability to analyze complex problems, identify suitable AI techniques, and apply critical thinking to develop innovative AI solutions.
|
|
S3
|
Acquire skills in collecting, preprocessing, and analyzing data to extract valuable insights and create meaningful representations for AI applications.
|
|
Values, Autonomy, and Responsibility
|
|
V1
|
Commitment to ethical considerations in data science, including privacy, fairness, and responsible data handling.
|
|
V2
|
Emphasis on collaborative work, fostering effective teamwork and communication skills in a data science context.
|