CSE-4125: Introduction to Machine Learning
Supervised and Unsupervised Learning, issues in machine learning: parametric and non-parametric models, curse of dimensionality, over-fitting, and model selection. Linear Models for Regression: Maximum Likelihood and least squares, regularized least squares, Bias variance decomposition, Bayesian linear regression. Linear Models for classification: Fisher's linear discriminant, probabilistic generative models -parametric (maximum likelihood and Bayesian) and non-parametric density estimation. Probabilistic discriminative models: logistic regression, log-linear models, Kernel methods and Sparse Kernel Machines. Clustering, mixture models and Expectation Maximization algorithm.Sequential data and Markov models. [Ref. https://stec.ac.bd]
Reference: 1. An Introduction to Machine Learning Click here to Download 2. An Introduction to Machine Learning (Yalın, Mustafa) Click here to Download 3. Machine learning and its Applications: A Review Click here to Download 4. Machine Learning Algorithms - A Review Click here to Download 5. Machine Learning: A Review of Learning Types Click here to Download
Lecture No.	Lecture Title	Description
1	Overview of Machine Learning
2	Applications of Machine Learning
3	Supervised Learning
4	Unsupervised Learning