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

Designing a curriculum or outlining subjects for a Bachelor of Computer Applications (BCA) program with a specialization in Artificial Learning (AL) and Machine Learning (ML) at Mandsaur University or any other educational institution involves covering fundamental aspects of computer science, mathematics and statistics. Additionally it includes courses focusing on AL and ML. Presented below is a list of significant topics that ideally should be included in such a program. This compilation aims to provide students with knowledge and skills in this field. 1. Introduction to Computer Science Concepts of Computers and Computing Basics of Programming using languages like Python, which is widely utilized in ML applications 2. Mathematics for Computer Science Linear Algebra (covering vectors, matrices, eigenvalues, etc.) Calculus (including integral calculus) Probability and Statistics (introduction to basics, distributions, hypothesis testing) Discrete Mathematics 3. Data Structures and Algorithms Essential Data Structures (such as arrays, lists, stacks, queues, trees, graphs) Algorithms (covering sorting techniques searching algorithms, complexity) Advanced Data Structures and Algorithms relevant to ML applications 4. Foundations of Machine Learning Introduction to the principles behind Machine Learning Comparison between Supervised vs. Unsupervised Learning approaches Understanding supervised learning methods along, with Reinforcement Learning techniques Evaluation Metrics overview; Accuracy measurements, precision recall tradeoff analysis including F1 score calculation 5. Deep. Neural Networks; Fundamentals of Neural Networks Different Architectures in Deep Learning such as Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) Backpropagation and Techniques for Optimization 6. Practical Machine Learning; Data Preprocessing and Feature Engineering Model Selection and Cross Validation Dealing with Overfitting, Underfitting and Model Fine tuning 7. Special Topics in Machine Learning; Natural Language Processing (NLP) Computer Vision Recommender Systems Analysis of Time Series Data 8. Ethics and Societal Impact of Artificial Intelligence (AI); Ethical Considerations in AI and Machine Learning Addressing Bias and Ensuring Fairness in Machine Learning Models Privacy, Security and Data Protection Concerns 9. Tools and Technologies; Python Programming for Machine Learning utilizing libraries like NumPy, Pandas, Matplotlib, Scikit Learn, TensorFlow and PyTorch. Management Systems for Databases Big Data Technologies such, as Hadoop and Spark. 10. Capstone. Internship