This course provides a comprehensive introduction to Artificial Intelligence (AI), exploring its fundamental concepts, techniques, and applications. Students will gain an understanding of the history, current state, and future potential of AI, as well as practical experience with AI tools and methodologies. Key topics include machine learning, neural networks, natural language processing, and ethical considerations in AI.
This module provides an overview of Artificial Intelligence, including its history, key concepts, and the various subfields within AI. Students will gain an understanding of the potential and limitations of AI technologies.
In this module, students will learn the basics of machine learning, including supervised and unsupervised learning, algorithms, and model evaluation. Practical exercises will help students apply these concepts using real-world data.
This module delves into neural networks and deep learning, covering topics such as network architectures, activation functions, and training techniques. Students will gain hands-on experience building and training neural networks.
Students will explore the field of Natural Language Processing (NLP), learning about text analysis, sentiment analysis, and language generation. Practical exercises will involve using NLP tools and libraries to process text data.
This module focuses on the practical applications of AI in various industries, including healthcare, finance, and robotics. Case studies and projects will provide students with insights into how AI is being used to solve real-world problems.
In this module, students will examine the ethical implications of AI technologies, including issues related to bias, privacy, and job displacement. Discussions will focus on developing responsible AI practices and policies.
This module explores the future directions and emerging trends in AI research and development. Students will learn about cutting-edge technologies and the potential impact of AI on society and the global economy.