Objectives of the Course:
To introduce students to the fundamental concepts, terminology, and scope of Artificial Intelligence and Machine Learning.
To develop understanding of core mathematical foundations such as statistics, probability, and linear algebra used in ML models.
To enable learners to perform data collection, cleaning, preprocessing, and exploratory data analysis effectively.
To familiarize students with major ML approaches including supervised, unsupervised, and reinforcement learning.
To provide hands-on experience in implementing basic ML algorithms using Python.
To build the ability to evaluate model performance and understand concepts like bias, variance, and generalization.
To prepare students for advanced AI and ML topics, including deep learning and applied AI systems.
What you'll learn
Module 1: Introduction to Artificial Intelligence
Hours: 4
- Definition, history, and evolution of AI
- Applications in various domains
- AI vs ML vs DL
- Intelligent agents & problem-solving basics
Module 2: Mathematical Foundations for ML
Hours: 8
- Linear Algebra fundamentals
- Probability & Statistics
- Matrices, vectors, distributions
- Gradient & optimization basics
Module 3: Data Handling & Preprocessing
Hours: 6
- Data types & sources
- Data cleaning, normalization, encoding
- Feature engineering
- Exploratory Data Analysis (EDA)
Module 4: Supervised Machine Learning
Hours: 10
- Regression algorithms
- Classification algorithms
- Decision Trees, SVM, KNN
- Practical implementation in Python
- Model performance metrics
Module 5: Unsupervised Machine Learning
Hours: 5
- Clustering (K-Means, Hierarchical)
- Dimensionality reduction (PCA)
- Applications & hands-on exercises
Module 6: Introduction to Neural Networks & Deep Learning
Hours: 4
- Perceptron & neural network basics
- Activation functions
- Introduction to CNN/RNN (conceptual)
- Applications of deep learning
Module 7: Model Evaluation & Deployment Basics
Hours: 3
Course Outcomes (COs)
After completing this course, students will be able to:
CO1: Explain the fundamental concepts, components, and applications of Artificial Intelligence and Machine Learning.
CO2: Apply essential mathematical concepts such as statistics, probability, and linear algebra to solve ML-related problems.
CO3: Perform data collection, preprocessing, feature engineering, and exploratory data analysis for model development.
CO4: Implement basic supervised and unsupervised machine learning algorithms using Python.
CO5: Evaluate machine learning models using appropriate metrics and understand issues like bias, variance, and over fitting.
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What is Artificial Intelligence (AI)?
AI is a branch of computer science that focuses on creating systems capable of performing tasks that require human intelligence, such as learning, reasoning, problem-solving, and decision-making. -
What are the foundations of AI?
The foundations of AI include mathematics, statistics, computer science, logic, psychology, neuroscience, and linguistics, which together support intelligent system design. -
Why is mathematics important in AI?
Mathematics provides the basis for algorithms, optimization, probability, linear algebra, and modeling used in machine learning and AI systems. -
What role does data play in AI?
Data is essential for training AI models, enabling systems to learn patterns, make predictions, and improve performance over time. -
What is the difference between AI and Machine Learning?
AI is the broader concept of intelligent machines, while machine learning is a subset of AI that enables systems to learn automatically from data. -
What are the main types of AI?
AI is classified into Narrow AI (task-specific), General AI (human-like intelligence), and Super AI (beyond human intelligence). -
What is knowledge representation in AI?
Knowledge representation involves structuring information so that AI systems can reason, understand relationships, and make decisions. -
What are AI algorithms?
AI algorithms are step-by-step procedures used to solve problems, make decisions, or learn from data, such as search algorithms and learning algorithms. -
What is the importance of ethics in AI?
Ethics ensures responsible AI development by addressing fairness, transparency, privacy, accountability, and societal impact. -
What are common applications of AI?
AI is used in healthcare, finance, education, robotics, natural language processing, computer vision, and autonomous systems.
