Introduction to Machine learning

Course Objectives: To understand the basic theory underlying machine learning. To be able to formulate machine learning problems corresponding to different real life applications. To understand a range of machine learning algorithms along with their strengths and weaknesses. To be able to apply machine learning algorithms, optimize models, evaluate their performance.

Coverage: Introduction to Machine Learning; Analytics lifecycle; Overview of the different types of data: structured, unstructured, and semi-structured. Data Distributions Supervised Learning and Linear Regression; Classification and Logistic Regression; Evaluation metrics for classification models: Accuracy, Precision, Recall, F1-score, and ROC curves Classification models - Na ̈ıve Bayes; Decision Tree and Random Forest; Support Vector Machine ML and MAP estimates. Bayes’ Optimal Classifier. PAC learnability and generalisation. Introduction to Graphical Models. Graph-cuts and spectral methods. Generative Vs. Discriminative Models. Unsupervised Learning; K-means clustering, Expectation Maximization, GMM Dimensionality reduction PCA and Feature Selection Introduction to Neural Networks and Deep Learning - CNN, LSTM Reinforcement Learning.

References: 1. Pattern Recognition and Machine Learning; Christopher M. Bishop
2. Pattern Classification (2nd ed.) Richard O. Duda, Peter E. Hart and David G. Stork; 1997
3. An Introduction to Statistical Machine Learning; Gareth James, Daniela Witten, Trebor Hastie, Robert Trebshirani, Jonathan Taylor; 2023
4. Machine Learning; Tom Mitchel, 1997
5. Deep Learning; Ian Goodfellow and Yoshua Bengio and Aaron Courville; MIT Press; 2016