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

Based on my update in April 2023 it may not be directly available or could vary when it comes to specific details about the BCA program in Artificial Learning & Machine Learning (AL ML) offered by Sarala Birla University. However I can provide you with a template based on similar programs that share common characteristics. To get the accurate and up to date information I recommend visiting the official website of Sarala Birla University or reaching out to them directly. Here are some key details about the program; The typical duration of the course is around 3 years. It is an undergraduate level program. The tuition fees can vary so please refer to the universitys website for exact figures. The mode of study is time. Sarala Birla University is an institute. It's important to note that tuition fees can differ based on factors such as residency status ( international) scholarship availability and specific fee structures set by the university. Additionally depending on what the university offers there might be options, like part time study modes. Always refer to Sarala Birla Universitys website for the most current and specific information regarding their programs.

As of my recent update in April 2023 it may not be possible to access the specific course outlines for Sarala Birla Universitys Bachelor of Computer Applications (BCA) program with a focus on Artificial Intelligence (AI) and Machine Learning (ML) known as AL ML. However I can provide you with an overview of the important topics typically covered in BCA programs that emphasize AI and ML. These topics are essential for understanding and applying principles and technologies related to AI and ML; 1. Introduction to AI and ML; Exploring the history and evolution of AI Understanding the concepts of AI, ML and Deep Learning (DL) Examining real world applications of AI and ML along with their future prospects 2. Programming for AI and ML; Gaining proficiency in Python programming language, which is widely used in the field Utilizing libraries such as NumPy, Pandas, Matplotlib, Scikit learn Exploring TensorFlow and PyTorch frameworks for deep learning applications 3. Mathematics for AI and ML; Building a foundation in linear algebra Developing an understanding of calculus principles Studying probability theory and statistics relevant to AI/ML Applying optimization techniques to enhance algorithms performance 4. Data Structures and Algorithms; Mastering various data structures essential, for efficient data management Acquiring problem solving skills through algorithm design techniques Analyzing time complexity to evaluate algorithm efficiency 5. **Data. Analysis**; Cleaning and preparing data Exploring and analyzing the data Creating and selecting features 6. **Machine Learning Algorithms**; Using supervised learning techniques such as linear regression, logistic regression, decision trees, random forests and support vector machines Employing learning methods like clustering and dimensionality reduction Understanding the basics of reinforcement learning 7. **Deep Learning**; Studying neural networks and their training process through backpropagation Exploring Convolutional Neural Networks (CNNs) for image analysis tasks Investigating Recurrent Neural Networks (RNNs) including LSTM and GRU models for sequential data processing Learning about Generative Adversarial Networks (GANs) for generating artificial data 8. **Natural Language Processing (NLP)**; Analyzing text using techniques for text processing tasks Conducting sentiment analysis to understand the emotional tone in textual data Building chatbots and language models to interact with users using natural language 9. **Computer Vision**; Applying image processing techniques to manipulate data Utilizing object detection algorithms to identify objects in images or videos Implementing face recognition systems for identifying individuals Generating synthetic images using advanced computer vision methods 10. **. Societal Implications of AI**; Considering ethical aspects during AI development and deployment Addressing issues related to bias, in AI systems Exploring the societal impact of artificial intelligence technologies

I don't have real-time access or specific details about course offerings at Sarala Birla University or any other institution's curriculum. However, I can provide you with a general list of important topics that are typically covered in a Bachelor of Computer Applications (BCA) program focusing on Artificial Intelligence (AI) and Machine Learning (ML). This list should be relevant to most courses in this field across various universities:

Important Topics in AI & ML for BCA
Introduction to Artificial Intelligence
Foundations of Machine Learning
Python Programming for AI & ML
Data Structures and Algorithms for AI & ML
Statistics and Probability for AI & ML
Neural Networks and Deep Learning
Natural Language Processing (NLP)
Computer Vision
Reinforcement Learning
AI & ML Project Management and Ethics

This table provides a concise overview of key areas in AI and ML that are commonly included in BCA programs with a focus on these subjects. For the most accurate and up-to-date information, please consult the official Sarala Birla University website or contact their admissions office directly.