Bachelor in Artificial Intelligence and Machine Learning

The BSc in Artificial Intelligence and Machine Learning aims to equip students with comprehensive knowledge and practical skills in AI and ML. The course fosters innovation, critical thinking, and technical expertise, preparing graduates to develop advanced AI solutions and contribute to the rapidly evolving tech landscape.

This course is designed for individuals aspiring to careers in AI, ML, data science, robotics, and related fields. It is suitable for aspiring AI professionals, software developers, data scientists, and anyone passionate about understanding and implementing AI technologies.

Key Knowledge and Skills: computer science, mathematics, and machine learning. Key skills include programming in Python and Java, data visualization, knowledge representation, neural networks, reinforcement learning, and ethical considerations in AI. Practical project work and interdisciplinary skills are emphasized, ensuring graduates are well-rounded and industry-ready.

Course Structure and Syllabus Overview:

Computer Science:

• Fundamental programming in Python and Java.
• Key topics: data structures, algorithms, databases, and data visualization.

Mathematics:

• Essential topics in linear algebra, calculus, discrete math, and statistics.

Machine Learning:

• Core methods in machine learning, neural networks, and reinforcement learning.
• Topics include supervised/unsupervised learning, deep learning, and dynamic programming.

Interdisciplinary AI:

• Insights from neuro-psychology, philosophy, and ethics.
• Critical analysis of AI’s societal impact.

Business Skills:

• Project management, entrepreneurship, game theory, and algorithmic business integration.

AI Applications:

• Focus on industrial optimization, robotics, natural language processing, and computer vision.

AI Project Work:

• Team-based projects, AI challenges, and a final bachelor thesis.

The course employs lectures, practical labs, tutorials, and project work. Assessments include exams, project reports, presentations, and participation in AI competitions. The practical approach ensures hands-on experience and real-world application of AI and ML concepts.

Duration: Full-time, part-time, or while working, with flexible mode switching.
Location: Main campus with some modules available online.
Format: Combines in-person and online learning with a strong emphasis on practical project work.
Fees: Detailed fee structure is available on the university’s website, with scholarships and financial aid options.
This comprehensive program prepares students to excel in various AI-related careers, fostering both technical proficiency and interdisciplinary insights.