RANDOM VARIABLE AND CART

CSC2MN107-Exploring Cyber Security in Social Media

25CSC2MN105EFFICIENT OFFICE DYNAMICS

Efficient office dynamics refer to the effective coordination of people, processes, and technology within an organization to achieve productivity, collaboration, and organizational goals. A well-organized office environment promotes clear communication, teamwork, and efficient workflow management, reducing delays and operational costs.

25-BCA3MN202-Foundations of Artificial Intelligence-NIM

AI built on contributions from several discipline computer science ,algorithms,data structures and neuro science provide creating intelligent systems.

25CSC3MN202_Introduction to AI and Machine Learning--VV

CO1 Explain the basic concepts of Artificial Intelligence1

CO2 Master Problem-Solving Techniques. Apply a problem solving technique to solve standard AI problems

CO3 Master various packages required to develop AI and machine learning applications

CO4 Understand few AI tools and an insight to Machine learning, Deep learning concepts 

CO5 Implement and analyse Machine learning algorithms to solve practical problems. 

CO6 Apply Concepts in Real-World Projects 

MODULE 1

Introduction to Artificial Intelligence & Problem Solving 



  1. Introduction to AI – Evolution of AI, AI problems, AI Techniques, AI Applications

  2. Various AI Domains (Introduction only) 

  3. Problem Solving Techniques - Search Algorithms, Knowledge representation and reasoning (Concepts only) 

  4. Problem Solving Techniques - constraint satisfaction problems, Game playing (Concepts only) 

  5. Problem Solving Techniques - Machine learning, Simulated Annealing (Concepts only)

Module 2

Introduction to Neural Networks

 

  1. Introduction to Artificial Neural Network 

  2. Understanding Brain & Perceptron Model

  3. Single Layer Perceptron Model & Learning in Single layer Perceptron

  4. Model Multi-Layer Perceptron Model & Learning in Multi-layer Perceptron Model   



Module 3

Python Packages for AI 

  1. Pandas 

  2. MatplotLib 

  3. Keras 

  4. Scikit-learn: 

Module 4

 

 Machine Learning Fundamentals 

 

  1. Introduction to Machine learning-

  2. Applications of Machine Learning 

  3. Supervised machine learning- Classification, regression (concepts only) 

  4. Unsupervised machine learning 

  5. clustering, Dimensionality Reduction (concepts only) 

  6. Basics of reinforcement learning

  7. Definition and history of deep learning 

  8. Key differences between traditional machine learning and deep learning