Fundamentals of AI and ML
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By
Dr. M. Maragatharajan
- 15/Nov/2024
- 6553 Students
Course Description
AI is one of the most fascinating and universal areas of computer science, with enormous future potential. AI has a tendency to make machines function like humans. You will gain knowledge from this course about the characteristics of agents, AI search techniques, and various software design techniques using Prolog. You'll learn about the potential, advantages, and restrictions of various artificial intelligence and machine learning techniques. To understand complex concepts and relate them to particular scenarios, you can employ a selection of AI and machine learning algorithms to address real-world challenges. The capacity to assess available learning strategies and choose the most effective ones to complete a task will also be provided by this course. Prolog programming allows you to create a variety of software agents. By the end of this course, you will be able to understand fundamentals of Artificial intelligence, machine learning, learning models & algorithms, prolog programming, software agents and etc.
Course Curriculum
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Session 1 - Introduction to AI
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Session 2 - AI vs ML vs DL
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Session 3 - History and Background
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Session 4 - Impact on foundations engineering and future directions
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Session 5 - Intelligent agents and environments
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Session 6 - Rational agents
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Session 7 - Nature of environments in AI
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Session 8 - Problem solving agents
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Fundamentals of AI and ML - Module 1 - Knowledge Checkpoint
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Session 1 - Search strategies - Uninformed
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Session 2 - Search strategies - Informed
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Session 3 - Adversal search
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Session 4 - Local search algorithms
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Session 5 - Knowledge representation
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Session 6 - Proposition and first order logic
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Session 7 - Prolog Introduction
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Session 8 - Prolog [Facts Simple code in prolog Variables]
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Session 9 - Prolog Conjunction, Disjunction & Structures
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Session 10 - Prolog Operators
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Session 11 - Prolog Lists, Searching & Backtracking
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Session 12 - Prolog Cut & Fail
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Session 1 - Probability theory and Linear algebra
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Session 2 - Convex optimization
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Session 3 - Statistical decision theory
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Session 4 - Probability for ML
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Session 5 - Random variables, Mean, Variance, Standard Deviation
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Session 6 - Common distributions
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Session 7 - Mean, variance and Joint distributions
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Session 8 - Data representations
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Session 9 - Feature learning and Applications
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Session 10 - Statistical inference for ML
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Session 1 - Introduction to machine learning
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Session 2 - Types of ML
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Session 3 - Perspectives and Issues in ML
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Session 4 - Introduction Classification
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Session 5 - Clustering
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Session 6 - Linear regression and its applications
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Session 7 - Underfitting and Overfitting
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Session 8 - Hyper values and validation sets
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Session 9 - Estimators, Bias and Variance
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Session 10 - Bayesian Statistics
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Session 11 - The curse of dimensionality
Dr. M. Maragatharajan
Assistant ProfessorDr. M. Maragatharajan, presently working as an assistant professor in the school of computing science and engineering at VIT Bhopal University, Madhya Pradesh.
₹999.00
₹2,999.00
Course includes:
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Level
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Beginner
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Duration
9h 13m
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Lessons
46
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Quizzes
1
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Certifications
Yes
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Language
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English