Course Title: Machine Learning (3 Cr.)
Course Code: CACS456
Class Load: 6 Hrs. I Week (Theory: 3Hrs. Practical: 3Hrs.)
Machine Learning presents comprehensive introduction to several topics on basic concepts and techniques of Machine Learning (ML). It also explores the understanding of the Supervised and unsupervised learning techniques, probability-based learning techniques, performance evaluation of ML algorithms and applications of ML.
Upon completion of this course, students should be able to 1. Explain the concept of supervised, unsupervised and semi-supervised learning. 2. Develop algorithms to learn linear and non- linear models using software. 3. Perform creative work in the field ML to solve given problem.
Unit 1: Introduction to machine learning 10Hrs
History of ML, Brain-neuron learning system, Definition and types of learning, need of ML, Data and tools, review of statistics, training, validation and test data, theory of learning – feasibility of learning – error and noise – training versus testing, generalization bound – approximation -generalization tradeoff – bias and variance – learning curve
Unit 2: Introduction to Supervised Learning 11 Hrs
Classification problems, Linear Regression – Predicting numerical value, finding best fit line with linear regression, Perceptron, learning neural networks structures, Decision tree representation, appropriate problems for decision tree learning, basic decision tree algorithm, support vector machines, separating data with maximum margin, Finding the maximum margin,
Unit 3: Bayesian and instance-based learning 11 Hrs
Probability theory and Bayes rule. Classifying with Bayes decision theory, Conditional Probability, Bayesian Belief Network, K-nearest neighbor
Unit 4: Introduction to un-supervised learning and dimensionality reduction 10 Hrs
Introduction to clustering, K- Mean clustering, different distance functions for clustering, Hierarchical clustering, Supervised learning after clustering, dimensionality reduction techniques, Principal component analysis
Unit 5: Measures for Performance Evaluation of ML algorithms
Classification accuracy, Confusion matrix Misclassification costs, Sensitivity and specificity, ROC curve, Recall and precision, box plot confidence interval
Laboratory work should be done covering all the topics listed above and a small project work should be carried out using the concept learnt in this course using software like matlab, python.
- Tom M Mitchell, Machine Learning, First Edition, McGraw Hill Education, 2013.
- Stephen Marsland, Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.
- Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, First Edition, Cambridge University Press, 2012.
To download full Syllabus CLICK HERE