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Core/Elective: Core Semester: 2 Credits: 4
Course Description

Machine learning is programming computers to optimize a performance criterion using example data or past experience. This course is to discuss many methods that have their bases in different fields: statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. Major focus of the course is on the algorithms of machine learning to help students to get a handle on the ideas, and to master the relevant mathematics and statistics as well as the necessary programming and experimentation

Course Objectives

To understand basics to advanced concepts of Machine Learning
To attain certain amount of statistical and mathematical sophistication to deal with the subject
To gain confidence in building Machine Learning algorithms and applications
To understand the multi-disciplinary aspect of the subject

Course Content

Machine Learning – Examples of Machine Learning applications – Supervised Learning: Learning a class from examples – Learning multiple classes – Regression – Model selection – Bayesian Decision Theory: Classification – Discriminant functions – Association rules – Parametric methods: MLE – Baye’s estimator – Parametric classification – Tuning model complexity

Multivariate Methods – Classification – Regression – Dimensionality reduction: LDA – PCA – Factor Analysis – ICA – Locally Linear Embedding – MDS- Probabilistic Learning: Gaussian Mixture Models- EM algorithm- Nearest Neighbor Methods – Distance Measures

Support Vector Machines: Optimal separation – Kernels – SVM algorithm – Extensions to SVM – Optimization and Search: Least-squares optimization – conjugate gradients – Search: Search techniques – Exploitation and exploration – Simulated annealing

Learning with trees: Decision trees – CART – Ensemble Learning: Boosting – Bagging – Random Forests – Unsupervised Learning: K-Means algorithm – Vector quantization – SOM algorithm – Markov Chain Monte Carlo Methods

Graphical Models: Bayesian Networks – Markov Random Fields – HMMS – Tracking Methods – Deep Belief Networks: Hopfield Network – Boltzmann Machine – RBM – Deep Learning


1. Ethem Alpaydin, Introduction to Machine Learning, 3e, MIT Press, 2014
2. Tom M. Mitchell, Machine Learning, McGraw Hill Education; 1e, 2017
3. Stephen Marsland, Machine Learning, An Algorithmic Perspective, 2e, CRC Press, 2015
4. Giuseppe Bonaccorso, Machine Learning Algorithms, 1e, Packt Publishing Limited, 2017
5. Ethem Alpaydin, Machine Learning- The New AI, MIT Press, 1e, 2016


1. Rohit Singh, TommiJaakkola, and Ali Mohammad.6.867 Machine Learning. Fall 2006. Massachusetts
Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu
2. Andrew Ng, https://www.coursera.org/learn/machine-learning

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