ESSENTIALS OF AI AND MACHINE LEARNING
CO1: Illustrate the pivotal role of machine learning in AI and elucidate the data pipelines crucial for machine learning.
CO2: Discuss machine learning algorithms for solving convex learning
CO3: Design classifier or regression models using various learning methods for a given problem setting.
Basics of AI and Machine learning
Artificial Intelligence: History and Applications. Examples of Machine Learning Applications, Learning Associations, Supervised Learning - Classification, Regression, Unsupervised Learning, Reinforcement Learning, VC Dimension.
Key components - Noise, Learning Multiple Classes, Model Selection and Generalization, hyper parameters, Parametric Methods - Maximum Likelihood Estimation, Parametric Classification, Tuning Model Complexity and Model Validation: Bias/Variance Dilemma
Pattern classification and Supervised methods
Bayesian Learning - Bayes Decision Theory, Minimum Error rate Classification, Classifiers, Discriminant functions and Decision Surfaces, Normal Density, Discriminant functions for the Normal Density, Bayes Decision Theory for Discrete features, Bayesian belief networks, Multivariate Data - Multivariate Classification, Multivariate Regression, Multiclass predictors- decision trees, random forest classifiers
Nonparametric techniques and unsupervised learning
Non Parametric Techniques - Density Estimation, Parzen Windows , K- Nearest Neighbor Estimation, Unsupervised Methods - Clustering Algorithms- K Means, Gaussian Mixture Models, Competitive Learning, Fuzzy Classification.
Linear Discriminant Functions - Linear Discriminant Functions and Decision Surfaces, Generalized Discriminant Functions, The two-category linearly separable case, non- separable behavior, Minimum Squared- Error procedures, Linear Methods : Linear regression, logistic regression, PCA, LDA
Neural networks, SVMs and Ensemble learning
Multi Layer Neural Networks - Feedforward Operation, Classification, Back – propagation Algorithm, Error Surfaces, Radial Basis Functions.
Kernel Machines: SVM Formulations, Optimal Separating Hyperplane, The Non Separable Case: Soft Margin Hyperplane, ν-SVM, Kernel Types, Kernel Machines for Regression
Ensemble - Model Combination Schemes, Voting, Error-Correcting Output Codes, Bagging, Boosting
1. S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
2. M. Mohri, A. Rostamizadeh, A. Talwalker, Foundations of Machine Learning, MIT Press, 2018.
3. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
4. Haykin, Simon. Neural networks and learning machines, 3/E. Pearson Education India, 2010.
5. Avrim Blum, John Hopcroft and Ravindran Kannan. Foundations of Data Science. Cambridge University Press 2017.
6. Charu C. Aggarwal, Linear Algebra and Optimization for Machine Learning, Springer, 2020.
7. Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning, Cambridge University Press, 2020.