Artificial Intelligence & Machine Learning Course Content

Session 1
• Introduction to machine learning
• Type of machine learning
• Application of machine learning – Case study
• Introduction to Python
• Installation of Python and related packages
Session 2
• Data structure in Python, objects and classes
• Introduction to various libraries in Python
• Various functions in Python
Session 3
• Introduction supervised machine learning
• Application of supervised machine learning – Case Study
• Introduction to different regression techniques
• Simple regression and multiple regression in Python – In class programming assignments
Session 4
• Introduction to Support Vector Regression
• Support Vector regression in Python – Programming Assignment
• Introduction to Decision Tree regression
• Decision tree regression exercise in Python
• Evaluating different regression models
Session 5
• Detailed understanding of Neural networks and its application
• Generative Learning algorithms
• Gaussian Discriminant Analysis
• Naïve Bayes
• Model Selection and feature selection in Python
Session 6
• Detailed understanding of Support Vector machine (SVM) and Kernel SVM
• Application of SVM in Python
• Application of Kernel SVM in Python
Session 7
• Detailed understanding of ensemble methods in Machine Learning
• Bragging and bosting
• Evaluating and debugging learning algorithms
• Bias/ Variance and trade-off between them
Session 8
• Application of Naïve Bias in Python – Programming exercise
• Detailed understanding of Union and Chernoff/ Hoeffding Bounds
• In class Quiz
Session 9
• Decision tree classification in Python
• Random Tree Classification in Python
• Practical application of Learning algorithms – Case Study
Session 10
• Introduction to Unsupervised machine learning
• Detailed understanding of various clustering techniques
• K-Means Clustering – Exercise in Python
• Hierarchal Clustering in Python – Exercise in Python
• Detailed understanding of EM and Mixture of Gaussians
Session 11
• Introduction to Factor analysis
• Principal component analysis – Exercise in Python
• Kernel PCA – Exercise in Python
Session 12
• Introduction to Reinforcement learning and control
• Detailed understanding of Upper confidence bound
• Upper confidence bound – exercise in Python
Session 13
• Thompson Sampling in Python
• Value Iteration and Policy Iteration
• Q-Learning and Value Function Approximation
Session 14
• Detailed understanding of NLP
• NLP in Python
Session 15
• Detailed understanding of Artificial Neural Networks and Convolutional Neural Networks
• Advanced machine learning concepts and its industry application
Session 16
• Final Project

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