The automatic scheduling or autonomous driving hasArtificial Intelligence in common with web search, speech recognition, and machine translation. These are complex real-world problems that span across various practices of engineering! Aim of artificial intelligence (AI) and Machine Learning (ML) is to tackle these problems with rigorous mathematical tools. The objective of this specialization of Computer Science and Engineering is to present an overview of the principles and practices of AI to address such complex real-world problems. The course is designed to develop a basic understanding of problem solving, knowledge representation, reasoning and learning methods of AI.Also, with the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this Computer Science and Engineering specialization we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
Emphasis is given to:
Probability Theory, Linear Algebra, Convex Optimization, Statistical Decision Theory - Regression, Classification, Bias Variance
Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
Linear Classification, Logistic Regression, Linear Discriminant Analysis, Perceptron, Support Vector Machines
Neural Networks - Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation
Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability Evaluation Measures
Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, Boosting
Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks
Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering
Gaussian Mixture Models, Expectation Maximization ,Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)
