The Complete 18cs71 | Artificial Intelligence And Machine Learning Notes

Description

VTU 18CS71 - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: Course Summary

Course Code: 18CS71

University: Visvesvaraya Technological University (VTU)

Summary:

• Artificial Intelligence (AI) and Machine Learning (ML) represent groundbreaking advancements in the realm of technology, revolutionizing the way computers learn, analyze data, and make decisions. AI refers to the creation of intelligent agents that can mimic human-like cognitive functions, while ML is a subset of AI focused on enabling systems to learn and improve from experience without explicit programming.
• In the vast landscape of AI, various techniques and approaches are employed, ranging from rule-based systems to neural networks. Machine Learning, on the other hand, encompasses a range of algorithms that empower machines to recognize patterns, make predictions, and adapt to new information. Key components of ML include supervised learning, unsupervised learning, and reinforcement learning, each serving distinct purposes in fostering computational intelligence.
• One of the primary strengths of AI and ML lies in their ability to process massive datasets and extract meaningful insights. This capability finds applications across diverse fields, including healthcare, finance, manufacturing, and entertainment. In healthcare, AI facilitates the analysis of medical images, diagnoses, and personalized treatment plans, while in finance, ML algorithms assist in fraud detection, risk assessment, and portfolio optimization.
• Furthermore, AI-driven technologies such as natural language processing (NLP) enable machines to comprehend and generate human language, leading to advancements in virtual assistants, chatbots, and language translation services. The integration of AI and ML is reshaping industries, automating tasks, and enhancing efficiency, ultimately contributing to the development of smart cities, autonomous vehicles, and innovative solutions to complex challenges.
• Despite their transformative potential, AI and ML also raise ethical considerations and concerns about bias in algorithms. Striking a balance between technological progress and ethical responsibility remains a crucial aspect of ongoing research and development in the field. As AI and ML continue to evolve, their impact on society, economy, and everyday life is poised to deepen, shaping the future of human-computer interaction and driving innovation across various domains.

Course Learning Objectives:

This course will enable students to:

• Explain Artificial Intelligence and Machine Learning
• Illustrate AI and ML algorithm and their use inappropriate applications

Module 1

What is artificial intelligence?, Problems, problem spaces and search, Heuristic search techniques

Module 2

Knowledge representation issues, Predicate logic, Representaiton knowledge using rules.

Concpet Learning: Concept learning task, Concpet learning as search, Find-S algorithm, Candidate Elimination Algorithm, Inductive bias of Candidate Elimination Algorithm.

Module 3

Decision Tree Learning: Introduction, Decision tree representation, Appropriate problems, ID3 algorith.

Aritificil Nueral Network: Introduction, NN representation, Appropriate problems, Perceptrons, Backpropagation algorithm.

Module 4

Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting, MDL principle, Bates optimal classifier, Gibbs algorithm, Navie Bayes classifier, BBN, EM Algorithm

Module 5

Instance-Base Learning: Introduction, k-Nearest Neighbour Learning, Locally weighted regression, Radial basis function, Case-Based reasoning. Reinforcement Learning: Introduction, The learning task, Q-Learning.