Applied Artificial Intelligence

Core Courses

Core Modules (Semester 1 & 2)

Semester 1:

• AI Techniques : Introduction to fundamental artificial intelligence paradigms, including search algorithms, knowledge representation, and intelligent agents.
• Data Mining- Theory and Practice- : Covers techniques for discovering patterns and knowledge from large datasets using clustering, classification, and association rules.
• Advanced Algorithms and Complexity : Analyzes algorithm design techniques and their computational complexity, preparing students to solve complex real-world problems efficiently.
• Metaheuristics and Evolutionary Algorithms : Explores optimization methods inspired by nature, such as genetic algorithms and particle swarm optimization, for solving NP-hard problems.
• Probability for AI : Introduces probabilistic reasoning, Bayesian inference, and stochastic models essential for handling uncertainty in AI systems.
• Python Programming : Develops practical programming skills in Python, focusing on data structures, libraries for AI, and scientific computing.
• Project Management in Computer Science : Teaches students how to plan, organize, and manage software development and AI projects using modern tools and methodologies.
• Entrepreneurship : Encourages innovation and entrepreneurial thinking by exploring startup development, business models, and technology commercialization.

Semester 2:

• Machine Learning : Focuses on supervised and unsupervised learning models, including SVMs, decision trees, and ensemble methods.
• Image Processing : Teaches the fundamentals of digital image analysis and enhancement techniques used in AI applications such as facial recognition.
• Natural Language Processing (NLP) : Introduces techniques for automatic text analysis, language modeling, part-of-speech tagging, and syntactic parsing.
• Speech Processing : Covers methods for speech recognition and synthesis, including signal processing and acoustic modeling.
• Data Visualization : Provides techniques for presenting complex data through interactive visual interfaces to support decision-making.
• Data Science / Data Analytics : Focuses on end-to-end data workflows, including data wrangling, statistical modeling, and predictive analytics.
• Blockchain Fundamentals : Introduces distributed ledger technologies and their integration in secure, transparent, and decentralized applications.
• E-commerce and Startups : Explores digital business strategies, platforms, and technologies supporting online commerce and startup ventures.

Advanced Topics

Advanced Modules (Semester 3)

• Deep Learning : Covers deep neural networks, convolutional and recurrent models, and their applications in vision, language, and audio processing.
• Information Retrieval : Explores the design of search engines, indexing methods, and relevance ranking for effective information access.
• Advanced Natural Language Processing : Delves into complex language understanding tasks such as sentiment analysis, machine translation, and language generation.
• Advanced Computer Vision : Focuses on object detection, image segmentation, and video analysis using state-of-the-art vision models.
• Bioinformatics : Applies AI techniques to biological data, such as DNA sequences and protein structures, enabling advances in health and genomics.
• Robotics : Introduces autonomous systems, control theory, and sensor integration to develop intelligent robots capable of interaction with the physical world.
• Scientific Writing (Theses & Articles) : Trains students to structure research papers and theses, write with clarity, and follow academic publishing standards.
• Academic Ethics : Examines ethical considerations in scientific research, including plagiarism, data integrity, and responsible AI use.