Postgraduate Diploma In Machine Learning And Artificial Intelligence

Module 1: Applied Machine Learning

Supervised Learning

Regression

Maximum Likelihood, Least Squares, Regularization

Bayesian Methods

Bayes Rule, MAP Inference, Active Learning

Foundational Classification Algorithms

Nearest Neighbors, Perceptron, Logistic Regression

Refinements to Classification

Kernel Methods, Gaussian Process

Intermediate Classification Algorithms

SVM, Trees, Forests and Boosting

Unsupervised Learning

Clustering Methods

K-Means Clustering, E-M, Gaussian Mixtures

Recommendation Systems

Collaborative Filtering, Topic Modeling, PCA

Sequential Data Models

Markov and Hidden Markov Models, Kalman Filters

Association Analysis

Clustering Methods - II

Model Comparisons, Analysis Considerations

Module 2: Applied Artificial Intelligence

Introduction to Artificial Intelligence

Intelligent Agents and Uninformed Search

Heuristic Search

Adversarial Search and Games

Constraint Satisfaction Problems

Reinforcement Learning

Logical Agents

AI applications: Natural Language Processing

AI Applications and Course Review

Module 3: Capstone Project