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
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).
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)
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.
