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Course Code: AIMI0201
Subject Name: Fundamentals of AI and ML
Course Content:
Unit 1: Introduction to Artificial Intelligence (AI): What is AI? Defining Intelligence. History and Evolution of AI. Branches of AI: Symbolic AI, Connectionist AI, etc. AI Applications and their Impact. Introduction to Intelligent Agents. Slides Slides
Unit 2: Introduction to Machine Learning: Supervised Learning: Regression vs. Classification. Unsupervised Learning: Clustering, Dimensionality Reduction. Reinforcement Learning: Basic Concepts, ML Workflow: Data Collection, Preprocessing, Model Training, Evaluation, Deployment.
Unit 3: Mathematical Foundations for ML: Linear Algebra Fundamentals: Vectors, Matrices, Matrix Operations (addition, multiplication, transpose). Dot Product, Probability and Statistics: Basic Probability, Bayes' Theorem, Descriptive Statistics (mean, median, mode, variance, standard deviation), Introduction to Probability Distributions (e.g., Gaussian). Calculus Basics: Derivatives, Gradients.
Unit 4: Data Preprocessing and Feature Engineering: Data Collection and Understanding. Handling Missing Data, Feature Scaling: Normalization and Standardization. Categorical Feature Encoding: One-Hot Encoding, Label Encoding. Basic Feature Engineering Concepts: Creating new features from existing ones. Exploratory Data Analysis (EDA) and Data Visualization.
Unit 5: Supervised Learning - Regression: Linear Regression: Simple Linear Regression, Multiple Linear Regression. Cost Function, Gradient Descent. Evaluation Metrics: Mean Squared Error (MSE), R-squared.
Unit 6: Supervised Learning - Classification: Logistic Regression: Binary Classification, Decision Boundaries. Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC Curve, Confusion Matrix. Basics of Decision Tree: Construction, Pruning. Slides
Unit 7: Unsupervised Learning - Clustering: K-Means Clustering: Algorithm, Choosing K. Applications of Clustering. Dimensionality Reduction: Principal Component Analysis (PCA). Slides
Unit 8: Ethical Considerations of AI/ML: Bias and Fairness in AI. Transparency and Explainability. Privacy and Data Security. Societal Impact of AI/ML: Job displacement, autonomous systems.
Resources:
Attendance: Verify at ERP (Mandatory 75% Attendance is required to appear in End-Sem Exams)
Exams:
Marks:
Course Code: AIMI0221
Subject Name: AI Programming and foundations Laboratory
Course Content:
1. Write Python programs for implementing Linear Regression
2. Write Python programs for implementing Logistic Regression
3. Write Python programs to implement k-Nearest Neighbors (kNN)
4. Write Python programs to implement Decision Trees (ID3 or CART)
5. Write a program Implement Support Vector Machine (linear kernel)
6. Write python program to implement K-Means Clustering
7. Write python program to implement Principal Component Analysis (PCA)
8. Implement Gradient Descent optimization for any of the above algorithms.
9. Implement multiple algorithms on a real dataset, evaluate and compare the performance.