Summary

This document is a lesson plan for a machine learning course. It covers various topics, including introduction to machine learning, regression, classification, clustering, neural networks, and case studies. It also includes details on the course objectives and outcomes. The document is targeted at undergraduate-level students.

Full Transcript

Machine Learning (CS31002) Lesson Plan Course: CS31002 (Machine Learning) Credits: 4 Session: December-2024 to May-2025 Coordinator: Dr. Partha Pratim Sarangi ([email protected]) Co-coordinator: Dr. Santos Kumar Baliarsingh ([email protected]) Course...

Machine Learning (CS31002) Lesson Plan Course: CS31002 (Machine Learning) Credits: 4 Session: December-2024 to May-2025 Coordinator: Dr. Partha Pratim Sarangi ([email protected]) Co-coordinator: Dr. Santos Kumar Baliarsingh ([email protected]) Course Objective 1. To provide a broad survey of different machine-learning approaches and techniques 2. To understand the principles and concepts of machine learning 3. To learn regression and classification models 4. To learn different clustering models 5. To understand artificial neural networks (ANN) and Convolutional neural networks (CNN) concepts 6. To develop programming skills that help to build real-world applications based on machine learning Course Outcomes Upon completion of the course, the students will be able to: CO1: Solve typical machine learning problems CO2: Compare and contrast different data representations to facilitate learning CO3: Apply the concept of regression methods, classification methods, and clustering methods. CO4: Suggest supervised /unsupervised machine learning approaches for any application CO5: Implement algorithms using machine learning tools CO6: Design and implement various machine learning algorithms in a range of real-world applications. Total Lectures ≈ 48 Before Mid-Sem ≈ 24 After Mid-Sem ≈ 24 Module 1 Lecture Topics 1 Introduction to Machine Learning, definition, and real-world applications. 2 Types of machine learning - Supervised, Unsupervised, Semi supervised learning, Definitions and examples. 3 Regression - Linear Regression, Intuition, Cost Function 4 Linear Regression - Gradient Descent 5 Multiple Linear regression 6 Closed-form Equation, Type of Gradient Descent (Batch, Stochastic, Mini-batch) - Definition, properties. 7 Normalization and Standardization (definition and why), Overfitting and Underfitting 8 Bias, Variance, Bias and Variance tradeoff 9 Regularization - Lasso Regularization, Ridge Regularization 10 ACTIVITY-1 Module 2 Lectur Topics e 11 Classification, Logistic Regression - 1 (binary) 12 Logistic Regression - 2 (binary) 13 Nearest neighbor and K Nearest Neighbour 14 Error Analysis - Train/Test Split, validation set, Accuracy, Precision, Recall, F-measure, ROC curve, Confusion Matrix 15 Naive Bayes Classifier - 1 16 Naive Bayes Classifier - 2 17 Decision Tree: Introduction, Id3 Algorithm - 1 18 Decision Tree - Id3 Algorithm - 2 19 Decision Tree - Problem of Overfitting, Pre-pruning/post-pruning Decision Tree, Examples. 20 Support Vector Machine - Terminologies, Intuition, Learning, Derivation - 1 21 Support Vector Machine - Terminologies, Intuition, Learning, Derivation - 2 22 Support Vector Machine - KKT Condition - 3 23 Support Vector Machine - Kernel, Nonlinear Classification, and multi-class (Basic concept) - 4 24 ACTIVITY-2 Mid Semester 25 Principal Component Analysis - Steps, merits, demerits, Intuition - 1 26 Principal Component Analysis - Steps, merits, demerits, Intuition - 2 27 Understanding and Implementing PCA using SVD for dimensionality reduction Module 3 Lecture Topics 28 Clustering: Introduction, K-means Clustering - 1 29 K-Median Clustering - 2 30 K-Means Clustering – 3 (Numerical) 31 DBSCAN Clustering - Why we use?, parameters, characterization of points, steps, determining parameters, time/space complexities 32 Mean Shift Clustering 33 Hierarchical Clustering - Agglomerative Clustering, Single/Complete/Average/Centroid Linkage 34 Hierarchical Clustering - Divisive hierarchical clustering 35 ACTIVITY-3 Module 4 Lecture Topics 36 Introduction Neural networks, McCulloch-Pitts Neuron 37 Least Mean Square (LMS) Algorithm 38 Perceptron Model 39 Multilayer Perceptron (MLP) and Hidden layer representation 40 Non-linear problem solving, Activation Functions 41 Backpropagation Algorithm - 1 42 Backpropagation Algorithm - 2 43 Exploding Gradient Problem and Vanishing Gradient Problem, why and how to avoid 44 Introduction to Convolutional Neural Network (CNN) 45 Basic idea about their working and structure 46 Data Augmentation, Batch Normalization, Dropout 47 ACTIVITY-4 Module 5 Lecture Topics 48 Introduce machine learning tools like Scikit Learn, PyTorch, TensorFlow, Kaggle competitions, etc. Case Study (Any Two) Case Study – 1: Classification using Iris Dataset Case Study – 2: Feature Extraction using PCA for Wine Dataset Case Study – 3: Implement linear regression to predict house prices based on features like size, location, and number of rooms. Case Study – 4: Clustering using Iris Dataset Case Study – 5: Classification of MNIST Dataset using CNN Model Activities Task Marks Before Mid-semester Quiz 5 Assignment / Coding Assignment 10 After Mid-semester Quiz 5 Assignment / Coding Assignment 10 Textbooks: 1. Madan Gopal, “Applied Machine Learning”, TMH Publication 2. Kevin P. Murphy, “Probabilistic Machine Learning”, MIT Press, 2023. 3. Ethem Alpaydin, “Introduction to Machine Learning”, Fourth Edition, MIT Press, 2010. Reference Books: 1. Laurene Fausett, “Fundamentals of Neural Networks, Architectures, Algorithms and Applications”, PearsonEducation, 2008. 2. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007. 3. Simon Haykin, “Neural Networks and Learning Machines”, Pearson 2008

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