BCA in Artificial Learning & Machine Learning (AL-ML) at YBN University - Course, Fees, Admission, Seats, Syllabus

Creating a detailed semester-wise syllabus for a specific course like the BCA in Artificial Learning & Machine Learning (AL-ML) at YBN University in a single line of HTML without access to the exact curriculum details would be speculative. However, I can provide a generic template that you can customize with the actual course details. Please adjust the content according to the specific syllabus of YBN University.

Semester	Subjects
Semester 1	Introduction to Programming, Mathematics for Computer Science, Introduction to Artificial Intelligence, English Communication Skills
Semester 2	Data Structures and Algorithms, Database Management Systems, Principles of Machine Learning, Soft Skills
Semester 3	Operating Systems, Computer Networks, Advanced Machine Learning, Python for Data Science
Semester 4	Web Technologies, Deep Learning, Natural Language Processing, Minor Project
Semester 5	Cloud Computing, Big Data Analytics, Reinforcement Learning, Elective 1
Semester 6	Robotics and Automation, Computer Vision, Elective 2, Major Project

This table is structured as follows:- The first row () defines the table headers () for the semester and the subjects.- Subsequent rows () represent each semester with two columns: one for the semester number and one for the list of subjects taught in that semester. Please replace the placeholder subjects with the actual subjects offered in the BCA in AL-ML course at YBN University for each semester.

As of my update in April 2023 the specific course details for YBN Universitys BCA program in Artificial Learning & Machine Learning (AL ML) may not be readily accessible without referring to the official curriculum provided by the university. However I can offer you an overview of essential subjects typically covered in a BCA program with a focus on Artificial Intelligence (AI) and Machine Learning (ML). These subjects form the foundation across universities and are likely to be included in YBN Universitys curriculum or similar programs; Introduction to AI and ML; Exploring the history, significance and current applications Programming Fundamentals for AI and ML (commonly using Python) Mathematics for AI and ML; Emphasizing algebra, calculus, probability and statistics In addition, to these foundational topics Machine Learning is extensively covered. This includes; Supervised Learning; Regression analysis, Decision Trees, Random Forests Support Vector Machines Unsupervised learning encompasses techniques such as clustering, dimensionality reduction and association rules. Deep learning involves networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Reinforcement learning covers the basics Q learning and policy gradient methods. When evaluating machine learning models common metrics include accuracy, precision, recall, F1 score and ROC AUC. In the field of intelligence (AI) search algorithms like BFS (breadth first search) DFS (depth first search) A* algorithm and greedy searches are commonly used. Knowledge representation and reasoning involve logic and ontologies. Natural Language Processing (NLP) techniques include tokenization, lemmatization, sentiment analysis and machine translation. Robotics covers the basics of robotics well as the application of AI in this field. Expert systems play a role in decision making. Practical applications of AI and machine learning include their use in web development, software development and data analysis for visualization purposes. Real world projects often involve building models for image recognition,text analysis and predictive modeling. The ethical implications of AI technologies are also explored with a focus on bias,fairness and ethical considerations. Emerging trends in AI technology include the integration of data analytics with machine learning the combination of Internet of Things (IoT) with AI technologies the intersection between quantum computingand AI and the applicationof edge computing, in the realmof AI. Various toolsand technologies are utilized in these fields to facilitate researchand development suchas Python programming language,RStudio,Jupyter Notebooks,TensorFlow and PyTorch. In the world of Python programming there are important libraries that play a significant role in data analysis and machine learning. These include NumPy, Pandas, Matplotlib, Scikit learn, TensorFlow and Keras. These libraries offer tools for handling and visualizing data effectively. Moreover when it comes to AI and ML applications in the cloud there are platforms like AWS, Google Cloud and Azure ML that provide robust solutions. These platforms offer a range of services and resources to support AI and ML projects. Overall this overview outlines the subjects necessary for individuals pursuing a Bachelors degree, in Artificial Intelligence and Machine Learning (AL ML).