Introduction to Artificial Intelligence and Data Science

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Module 1
Introduction to AI and Machine Learning: Basics of Machine Learning -types of Machine Learning systems-challenges in ML- Supervised learning model example- regression models- Classification model example- Logistic regression-unsupervised model example- K-means clustering. Artificial Neural Network- Perceptron- Universal Approximation Theorem (statement only)- Multi-Layer Perceptron- Deep Neural Network- demonstration of regression and classification problems using MLP
5
Module 2
Mathematical Foundations of AI and Data Science: Role of linear algebra in Data representation and analysis – Matrix decomposition- Singular Value Decomposition (SVD)- Spectral decomposition- Dimensionality reduction technique-Principal Component Analysis (PCA).
3
Module 3
Applied Probability and Statistics for AI and Data Science: Basics of probability-random variables and statistical measures - rules in probability-Bayes theorem and its applications- statistical estimation-Maximum Likelihood Estimator (MLE) - statistical summaries- Correlation analysis linear correlation (direct problems only)- regression analysis- linear regression (using least square method)
5
Module 4
Basics of Data Science: Benefits of data science-use of statistics and Machine Learning in Data Science- data science process, applications of Machine Learning in Data Science- modelling process- demonstration of ML applications in data science- Big Data and Data Science.
3