Artificial Intelligence
This course is offered at Yachay Tech University under the School of Mathematical and Computational Sciences.
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Course Description:
This course provides an in-depth study of the theories, algorithms, and applications of artificial intelligence (AI). The course covers a broad range of topics in AI, including intelligent agents, problem-solving, search algorithms, probability, and machine learning.
It then covers the basic concepts of AI, including machine learning, reinforcement learning, decision trees, neural networks, and Bayesian learning.
The course emphasizes hands-on experience with AI tools and frameworks using Python.
Upon completion of the course, students will have a comprehensive understanding of AI theory and practice, and be able to apply this knowledge to real-world problems in a variety of industries. The course is designed for students with a strong background in computer science or related fields, and requires a solid foundation in calculus, linear algebra, probability theory, and Python.
The course begins by introducing the basic concepts of AI, including problem-solving, search algorithms, and heuristic techniques. It then covers the fundamental principles of machine learning, including supervised and unsupervised learning, decision trees, and neural networks. The course also explores natural language processing, including text processing, sentiment analysis, and machine translation.
Another important aspect of the course is the study of robotics, including the basic principles of kinematics, dynamics, and control of robotic systems. The course also introduces computer vision, including image processing, feature extraction, and object recognition.
The course emphasizes hands-on experience with AI tools and frameworks, including Python programming language. Students will have opportunities to apply the concepts and techniques learned in the course to real-world problems through projects and assignments.
Upon completion of the course, students will have a solid understanding of the foundational concepts of AI and the ability to apply them to solve real-world problems. The course is designed for undergraduate students with a strong background in computer science and mathematics, and no prior knowledge of AI is required.
Course contribution to professional training:
This course offers a valuable contribution to professional training by equipping individuals with the skills and knowledge necessary to work with and leverage the power of AI technologies in various industries.
This course offers numerous technical contributions to the professional field of graduate students, including:
Building a comprehensive overview of AI, including its history, key concepts, and fundamental algorithms. This foundational knowledge helps individuals understand the basic principles underlying AI, which is essential for developing and implementing AI applications.
Learning a range of AI techniques and algorithms, such as machine learning, deep learning, natural language processing, robotics, and computer vision. These techniques are vital for building advanced AI models that can solve complex problems and automate various tasks, such as data analysis, autonomous vehicles, image recognition, and language translation.
Being aware of the ethical and societal implications of AI, including bias, fairness, and transparency. This aspect of the course is crucial in ensuring that individuals working with AI technologies are aware of the potential risks and can design and deploy AI systems that are ethical and inclusive.
Provide hands-on experience in developing and deploying AI models using popular AI tools and frameworks with Python. This practical aspect of the course is crucial for individuals to gain the necessary skills and confidence to work with AI technologies in real-world settings.
Advanced knowledge of AI algorithms and techniques, such as machine learning, deep learning, and natural language processing, which can be applied to various industries, such as healthcare, finance, and marketing.
Practical experience in building and deploying AI models using popular AI tools and frameworks, such as TensorFlow, Keras, and PyTorch, which are in high demand in the job market.
Mastery of programming languages such as Python and statistical concepts such as linear algebra, calculus, and probability theory, which are essential for developing AI models.
The ability to analyze and interpret large and complex datasets, identify patterns and insights, and make data-driven decisions using AI models.
Knowledge of ethical considerations and potential biases in AI systems, which can be used to design and deploy ethical and inclusive AI models.
Skills to collaborate with interdisciplinary teams, communicate technical concepts effectively to non-technical stakeholders, and manage AI projects from ideation to deployment.
An understanding of cutting-edge AI research and development trends, which can be used to stay up-to-date with the latest innovations and developments in the field.
The ability to design and implement advanced AI techniques, such as reinforcement learning and generative models, to solve complex problems in various domains, which can contribute to innovation and disruption in the industry.
Experience with optimizing AI models using hyperparameter tuning, regularization techniques, and performance metrics to improve their accuracy and efficiency, which can lead to cost savings and increased productivity.
The ability to apply AI techniques to solve real-world problems, such as predicting consumer behavior, diagnosing diseases, and optimizing logistics, which can contribute to social and economic development.
Course objectives:
This course aims to provide students with a comprehensive understanding of AI and its applications. Some of the key course objectives are:
To introduce the fundamental concepts of AI, including its historical context, evolution, and potential applications.
To provide an overview of the various techniques and algorithms used in AI, such as machine learning, deep learning, natural language processing, robotics, and computer vision.
To enable students to understand and apply various AI models and techniques to solve real-world problems.
To provide hands-on experience in designing, implementing, and evaluating AI models using Python.
Apply data preprocessing techniques, such as data normalization and feature scaling, to prepare data for AI modeling.
To develop critical thinking skills by analyzing and evaluating various AI applications and their impact on society.
To prepare students to work with AI technologies in various industries, such as healthcare, finance, and transportation.
Build intelligent agents and robotics systems using reinforcement learning.
To foster teamwork and collaboration skills by working on group projects that involve developing and deploying AI models.
To encourage lifelong learning by providing resources and opportunities for students to keep up with the latest AI developments and trends.
To explore the ethical and societal implications of AI, including ethical considerations, potential biases, and fairness in AI systems; as well as the importance of designing ethical and inclusive AI systems.
Apply AI techniques to solve real-world problems, and communicate findings and results effectively to diverse audiences.
Analyze and visualize data to gain insights and select appropriate AI algorithms for solving specific problems.
Optimize AI models using hyperparameter tuning, regularization techniques, and performance metrics to improve their accuracy and efficiency.
Design and implement advanced AI techniques, such as reinforcement learning and generative models, to solve complex problems in various domains.
Curricular Units:
UC.0
Course Overview
UC.1
Introduction to Artificial Intelligence
UC.2
Informed and Local Search
UC. 3
Logic and Planning
UC. 4
Constraint Satisfaction Problems
UC. 5
Search Games
UC. 6
Markov Decision Processes
UC. 7
Reinforcement Learning
UC. 8
Bayes Networks I
UC. 9
Bayes Networks II
UC. 10
Markov Models and Filtering
UC. 11
Rationality
UC. 12
Decision Networks
UC. 13
MLP and Neural Networks
UC. 14
Ethics and Advanced Topics on AI