III-I CSE(AIML) ESSENTIALS OF MACHINE LEARNING Syllabus

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 ESSENTIALS OF MACHINE LEARNING

Course Outcomes:

At the end of this course, students will be able to:

1. Understand the Concepts of Machine Learning.

2. Develop Simple Regression Models.

3. Build various classification algorithms.

4. Analyse the need of ensemble learning and dimension reduction.

5. Apply the Clustering algorithms for developing applications.

Unit – I: Introduction to Machine Learning

What is Machine Learning, Types of Machine Learning, Applications of Machine learning, preparing to Model, Modelling and Evaluation

Unit – II: Supervised Learning: Regression

Introduction to Regression, Example of Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Regression Analysis, Improving the accuracy of the Linear Regression Model, Ridge Regression, Lasso Regression.

Unit – III: Supervised Learning: Classification

What is Classification, General Approach to Classification, K-Nearest Neighbour Algorithm, Logistic Regression, Decision Trees: Construction, classification and regression trees, example, Naive Bayesian Classifier, Support Vector Machines: Optimal Separation, Kernels, Algorithm.

Unit – IV: Unsupervised Learning and Dimensionality Reduction

Types of Unsupervised Learning, Challenges in Unsupervised Learning, Clustering Algorithms: K-Means, Agglomerative, DBSCAN, Comparing and Evaluating Clustering Algorithms. Dimensionality Reduction: Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Factor Analysis (FA).

Unit – V: Ensemble Learning and Reinforcement Learning

Ensemble Methods: Bagging, Boosting & Random Forests, Reinforcement Learning: Overview, Example: Getting Lost, Markov Decision Process, Values, difference between SARSA and Q Learning, Uses of Reinforcement Learning

 

Text Books:

1.     Machine Learning, Saikat Dutt, Subramanian Chandramouli and Amit Kumar Das, Pearson, 2018.

2.     Machine Learning: An Algorithmic Perspective by Stephen Marsland, CRC Press, 2009.

 

Reference Books:

1.     Introduction to Machine Learning (Adaptive Computation and Machine Learning), Ethem Alpaydin, The MIT Press 2004.

2.     Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition Aurélien Géron, 2019, O'Reilly Media, Inc. 

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