AI301Semester VIII • BTECH-CSIT
Data Science, Machine Learning & AI
Statistical learning theory, gradient descent dynamics, linear regression, logistic classification, decision trees, neural networks, and model evaluation.
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Units
2
Topics
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Handbooks
OFFICIAL COURSE TEXTBOOKIDRAS Master Book & Interactive Engine
Data Science, Machine Learning & AI (AI301): Complete Digital Textbook & Labs
Comprehensive curriculum-aligned chapters, embedded interactive simulators, deep micro-concepts, and university exam/viva solutions.
Coding Practice Chapter Modules
Hands-on Practice Modules (1 Chapters)
Progressive coding topics taught through live code examples, exercises, and execution traces.
UNIT 1 INTERACTIVE LABORATORYLive Interactive Sim
C Struct Memory Alignment & Hardware Padding Simulator
Supervised vs unsupervised learning, loss functions, convexity, gradient descent dynamics, and learning rate scheduling.
Topic 1INTERMEDIATE~35 mins
Loss Functions, Convex Optimization & Gradient Descent Dynamics
Mathematical formulation of Mean Squared Error, gradient calculation, learning rate schedules, and convergence guarantees.
Topic 1FOUNDATION~35 mins
Supervised Learning Formulations, Convex Loss & Gradient Descent Optimization
Mathematical formulation of empirical risk minimization, Mean Squared Error (MSE), learning rates, and gradient convergence.