📖 Course Overview
This course provides students with a solid foundation in the concepts, methods, and tools of modern Artificial Intelligence (AI). It integrates theoretical principles with practical applications across machine learning, deep learning, knowledge representation, and intelligent systems. Through hands-on projects, case studies, and interactive sessions, students will learn how to design, implement, and evaluate AI algorithms while critically examining their limitations, ethical implications, and societal impact.
🔑 Key Topics Covered
Historical development of AI, key definitions, major paradigms, and current applications across domains
Essential linear algebra, probability and statistics, optimization concepts, and algorithmic complexity
State-space representation, uninformed and informed search, constraint satisfaction problems, and basic planning
Logic-based representations, inference, reasoning under uncertainty, ontologies, and rule-based systems
Learning paradigms, model training and evaluation, overfitting and generalization, bias–variance trade-off
Linear and logistic regression, k-NN, decision trees, random forests, SVMs, evaluation metrics
Clustering, dimensionality reduction, anomaly detection, and feature engineering
Neural network architectures, backpropagation, optimization algorithms, regularization techniques
Text classification, sentiment analysis, language modeling, image classification, object detection
Data pipelines, model serving, monitoring, and lifecycle management
Algorithmic bias, transparency, accountability, privacy, explainability, regulatory frameworks
Group projects, case studies, generative AI, large language models, and autonomous systems
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