BCA in Artificial Learning & Machine Learning (AL-ML) at Moodlakatte Institute Of Technology - Course, Fees, Admission, Seats, Syllabus

Highlight	Details
Course Duration	3 Years
Course Level	Undergraduate
Course Tuition Fees	Rs [Your Data Here]
Mode of Study	Full Time
Institute Type	Private

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Semester	Subjects
1st Semester	Introduction to Programming, Mathematics for Computer Science, Introduction to Artificial Intelligence
2nd Semester	Data Structures, Database Management Systems, Principles of Machine Learning
3rd Semester	Algorithms, Computer Networks, Deep Learning Fundamentals
4th Semester	Operating Systems, Statistical Methods for Machine Learning, Natural Language Processing
5th Semester	Advanced Machine Learning, Big Data Analytics, Elective I
6th Semester	Project Work, Elective II, Elective III

Please replace the subjects listed with the actual subjects from the syllabus once you have the correct information. This table is just a generic example and might not accurately reflect the curriculum of the BCA in AL-ML program at Moodlakatte Institute Of Technology.

Based on my recent update in April 2023 specific information regarding the curriculum of the BCA program in Artificial Learning & Machine Learning (AL ML) at Moodlakatte Institute Of Technology may not be readily accessible. However I can offer an overview of key subjects typically included in BCA programs that focus on Artificial Intelligence (AI) and Machine Learning (ML) across different educational institutions. These subjects serve as knowledge for individuals entering the realms of AI and ML; **Introduction to Artificial Intelligence (AI) and Machine Learning (ML);** Covers the basics of AI and ML their historical context and how they have evolved over time. **Programming for AI and ML;** Provides an introduction to programming languages commonly utilized in AI and ML applications, such as Python, R along with their associated libraries like NumPy, Pandas and Matplotlib for Python. **Mathematics for AI and ML;** Delves into the concepts essential, for understanding AI and ML principles. Linear algebra, calculus, statistics and probability theory are crucial for grasping and creating machine learning models. **Data. Algorithms;** Knowledge of various data structures (such as arrays, lists, stacks, queues, trees, graphs) and algorithms (including sorting and searching) along with their relevance in AI and ML. **Machine Learning Algorithms;** Covering learning (like linear regression, logistic regression, decision trees, random forests, support vector machines) unsupervised learning (including clustering and dimensionality reduction) and reinforcement learning. **Deep Learning;** Introduction to networks, convolutional neural networks (CNNs) recurrent neural networks (RNNs) as well as deep learning frameworks like TensorFlow and Keras. **Natural Language Processing (NLP);** Basics of NLP encompassing text preprocessing, sentiment analysis, chatbots creation and machine translation. **Computer Vision;** Exploring the basics of image processing along with object detection techniques like recognition alongside practical applications in computer vision. **. Social Implications of AI;** Addressing concerns related to biases in AI systems privacy issues arising from AI technologies usage along with discussing the social impact of AI innovations. **AI and ML Project Management;** Understanding project planning aspects including data gathering procedures model selection considerations training evaluation methods deployment strategies for AI/ML projects. **Emerging Trends in AI and ML;** Introducing recent advancements research areas within the realm of artificial intelligence/machine learning such as adversarial networks (GANs) developments in reinforcement learning applications, across healthcare finance autonomous driving sectors.