Course Code | CS 360 |

Course Name | Foundations of Machine learning |

Offered to | UG/PG |

Pre-requisites | CS230 Probability and Statistics for CS |

Lecture | 3 |

Tutorial | 0 |

Practical | 0 |

Credit | 6 |

Reference | Suggested Textbooks: 1. Machine Learning, T. Mitchell, 2009. Reference Texts: 1. Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman, 2017. 2. Pattern Recognition and Machine Learning, C. Bishop, 2010. 3. Foundations of Data Science, M. Blum, J. Hopcroft, and R. Kannan, 2018. 4. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, H. Wickham and G. Grolemund, 2016. |

Description | Objective: To equip students to apply machine learning methods to real-world applications such as recommender systems, computer vision, bioinformatics, and text mining. Prerequisite: Contents: • Data science basics, how to wrangle, visualize, and analyze data, using models to explore your data • Supervised learning, linear and logistic regression, generative learning, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation, support vector machines, artificial neural networks and deep learning. • Unsupervised learning, mixture of Gaussians, EM algorithm, autoencoders • Bias-variance tradeoff, regularization and model selection • Dimensionality reduction, principal component analysis, singular value decomposition |