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

Course Duration	Course Level	Course Tuition Fees	Mode of Study	Institute Type
[Insert Duration Here]	[Insert Level Here]	[Insert Fees Here]	[Insert Mode of Study Here]	[Insert Institute Type Here]

Please replace the placeholders (e.g., [Insert Duration Here]) with the specific details of the Silver Lining BCA in Artificial Learning & Machine Learning (AL-ML) specialization once you have them.

Semester	Subjects/Topics
Semester 1	Introduction to Programming, Mathematics for Computer Science, Introduction to Artificial Intelligence, English for Communication, Environmental Studies
Semester 2	Data Structures and Algorithms, Object-Oriented Programming, Database Management Systems, Principles of Machine Learning, Soft Skills
Semester 3	Operating Systems, Computer Networks, Advanced Data Structures, Statistical Methods for Machine Learning, Elective 1 (Introduction to Robotics/Quantum Computing)
Semester 4	Design and Analysis of Algorithms, Cloud Computing, Deep Learning, Natural Language Processing, Elective 2 (Blockchain Technology/Cyber Security Fundamentals)
Semester 5	Big Data Analytics, Reinforcement Learning, Computer Vision, Project Work (Part 1), Elective 3 (Internet of Things/Advanced Database Management Systems)
Semester 6	Artificial Intelligence in Healthcare, Ethics in AI, Advanced Machine Learning Projects, Project Work (Part 2), Elective 4 (Game Development/AR and VR Technologies)

This table is a broad and hypothetical curriculum that aims to cover essential areas in AI and ML within a BCA program. Actual course offerings, names, and structures will vary by institution.

As of my update in April 2023 I don't have direct access to specific course outlines for "Silver Lining BCA in Artificial Learning & Machine Learning (AL ML)." However I can provide you with an overview of important topics typically covered in Bachelor of Computer Applications (BCA) programs that focus on Artificial Intelligence (AI) and Machine Learning (ML). These topics are essential, for students looking to specialize in these fields; I have experience in programming for AI and ML using tools like Python as well as libraries such as NumPy, Pandas and Matplotlib. Additionally I am familiar with machine learning frameworks like TensorFlow, PyTorch and Scikit learn. In terms of machine learning concepts I have worked with supervised learning methods such as regression, decision trees, SVMs (Support Vector Machines) and nearest neighbors. I am also experienced in learning techniques including clustering, dimensionality reduction and association rules. Furthermore I have knowledge of learning approaches such as random forests boosting techniques and bagging methods. When it comes to evaluating the performance of machine learning models I am well versed in metrics like accuracy, precision, recall (sensitivity) F1 score ( mean) and ROC AUC (Receiver Operating Characteristic. Area Under the Curve). In the realm of learning specifically; I have a solid understanding of neural networks including their basics, architecture design principles, and backpropagation algorithm. My expertise extends to Convolutional Neural Networks (CNNs) which're highly effective for image processing tasks and feature extraction. Lastly but not leastly Recurrent Neural Networks (RNNs) are also, within my knowledge domain. In the field of data analysis we have time series analysis and natural language processing. When it comes to generating images and transferring styles there are Generative Adversarial Networks (GANs). Natural Language Processing (NLP) involves techniques such as tokenization, stemming and lemmatization for text processing. We also have word embeddings like Word2Vec and GloVe that help us understand the meaning of words in a manner. Sequence models like RNNs, LSTMs and attention mechanisms are used for analyzing data. Moving on to reinforcement learning it's about understanding the basics of agents, environments, states, actions and rewards. Advanced models such as Q learning, policy gradient methods and deep reinforcement learning take it further. Ethics plays a role in AI with considerations like bias detection/fairness assessment/ transparency enhancement/ accountability assurance being crucial aspects. Looking ahead at the trends in AI and ML we have quantum computing advancements impacting AI systems across various fields such as healthcare or autonomous vehicles. Lastly practical applications involve implementing AI and ML models to solve real world problems through project work or analyzing successful case studies, from industries.