IL4008 Machine Learning Syllabus:
IL4008 Machine Learning Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the concepts and mathematical foundations of machine learning and types of problems tackled by machine learning.
To explore the different supervised learning techniques including ensemble methods
To outline different aspects of unsupervised learning and reinforcement learning
To outline the role of probabilistic methods for machine learning
To understand the basic concepts of neural networks and deep learning
UNIT I INTRODUCTION AND MATHEMATICAL FOUNDATIONS
What is Machine Learning? Need –History – Definitions – Applications – Advantages, Disadvantages & Challenges -Types of Machine Learning Problems – Mathematical Foundations – Linear Algebra & Analytical Geometry -Probability and Statistics -Vector Calculus & Optimization -Information theory.
UNIT II SUPERVISED LEARNING
Introduction-Discriminative and Generative Models -Linear Regression -Least Squares -Under fitting / Over-fitting -Cross-Validation – Lasso Regression-Classification -Logistic Regression Gradient Linear Models -Support Vector Machines –Kernel Methods -Instance based Methods – K-Nearest Neighbours – Tree based Methods –Decision Trees –ID3 – CART – Ensemble Methods –Random Forest – Evaluation of Classification Algorithms.
UNIT III UNSUPERVISED LEARNING AND REINFORCEMENT LEARNING
Introduction – Clustering Algorithms -K – Means – Hierarchical Clustering – Cluster Validity – Dimensionality Reduction –Introduction -Principal Component Analysis – Recommendation Systems – EM algorithm. Reinforcement Learning – Elements -Model based Learning – Temporal Difference Learning.
UNIT IV PROBABILISTIC METHODS FOR LEARNING
Introduction -Naïve Bayes Algorithm -Maximum Likelihood -Maximum Apriori -Bayesian Belief Networks -Probabilistic Modelling of Problems -Inference in Bayesian Belief Networks – Probability Density Estimation – Sequence Models – Markov Models – Hidden Markov Models.
UNIT V NEURAL NETWORKS AND DEEP LEARNING
Neural Networks – Biological Motivation- Perceptron – Multi-layer Perceptron – Feed Forward Network – Back Propagation-Activation and Loss Functions- Limitations of Machine Learning – Deep Learning – introduction – Convolution Neural Networks – Recurrent Neural Networks – LSTM- Use cases.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
CO1: Understand and outline problems for each type of machine learning
CO2: Design a Decision tree and Random forest for an application
CO3: Implement Probabilistic Discriminative and Generative algorithms for an application and analyze the results.
CO4: Use a tool to implement typical Clustering algorithms for different types of applications.
CO5: Design and implement an HMM for a Sequence Model type of application.
REFERENCES:
1. Probabilistic Machine Learning: An Introduction by Kevin Murphy, MIT Press 2022.
2. Kevin Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
3. Peter Flach, “Machine Learning: The Art and Science of Algorithms that Make Sense of Data”, First Edition, Cambridge University Press, 2012.
4. Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, Chapman and Hall/CRC Press, Second Edition, 2014
5. EthemAlpaydin, “Introduction to Machine Learning”, Third Edition, Adaptive Computation and Machine Learning Series, MIT Press, 2014
6. Tom M Mitchell, “Machine Learning”, McGraw Hill Education, 2013
7. Shai Shalev-Shwartz and Shai Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge University Press, 2015