Syllabus Artificial Intelligence Pdf Machine Learning Artificial Scheme of instruction department of computer science and engineering (artificial intelligence & machine learning). Ai and ml syllabus free download as pdf file (.pdf), text file (.txt) or read online for free. this document outlines the course contents and objectives of a course on artificial intelligence and machine learning.
Ai Syllabus Pdf Machine Learning Artificial Intelligence Introduction to machine learning: history of ml, ai vs. ml, types of learning (supervised, unsupervised, semi, weak, self, etc.). types of data: tabular, image, video, audio, sequential, etc. feature engineering, ml approaches: introduction to regression, classification, clustering. Artificial intelligence & machine learning syllabus (common core syllabus under cbcs) with effect from the academic year: 2025 2026 and onwards. Machine learning offered by the department of computer engineering, national institute of technology, kurukshetra w.e.f. 2023 24 (1st & 2nd year). To strengthen the theoretical, practical and ethical dimensions of the learning process by fostering a culture of research and innovation among faculty members and students.
Artificial Intelligence And Machine Learning Pptx Machine learning offered by the department of computer engineering, national institute of technology, kurukshetra w.e.f. 2023 24 (1st & 2nd year). To strengthen the theoretical, practical and ethical dimensions of the learning process by fostering a culture of research and innovation among faculty members and students. Introduction to decision tree, decision tree representation, decision tree learning, appropriate problems for decision tree learning, basic decision tree learning algorithm, id3, entropy and information gain, hypothesis space search in decision tree learning, inductive bias in decision tree learning, issues in decision tree learning. Through projects done in lab, increase the true hands on student learning experience and enhance their conceptual understanding, increase students’ ability, competence and teamwork skills on dealing with real life engineering problems. Apply an unsupervised learning algorithm (e.g., k means clustering, hierarchical clustering) to a dataset. explore the resulting clusters and interpret the findings. Learning objectives: to apply the concepts of machine learning to solve real world problems and to implement basic algorithms in clustering & classification applied to text & numeric data.
Ai 2021r Ai Ds Syllabus Pdf Artificial Intelligence Intelligence Introduction to decision tree, decision tree representation, decision tree learning, appropriate problems for decision tree learning, basic decision tree learning algorithm, id3, entropy and information gain, hypothesis space search in decision tree learning, inductive bias in decision tree learning, issues in decision tree learning. Through projects done in lab, increase the true hands on student learning experience and enhance their conceptual understanding, increase students’ ability, competence and teamwork skills on dealing with real life engineering problems. Apply an unsupervised learning algorithm (e.g., k means clustering, hierarchical clustering) to a dataset. explore the resulting clusters and interpret the findings. Learning objectives: to apply the concepts of machine learning to solve real world problems and to implement basic algorithms in clustering & classification applied to text & numeric data.
Artificial Intelligence Syllabus Pdf Apply an unsupervised learning algorithm (e.g., k means clustering, hierarchical clustering) to a dataset. explore the resulting clusters and interpret the findings. Learning objectives: to apply the concepts of machine learning to solve real world problems and to implement basic algorithms in clustering & classification applied to text & numeric data.
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