EI25402 Applied Machine Learning Syllabus:

EI25402 Applied Machine Learning Syllabus – Anna University UG Syllabus Regulation 2025

Course objective:

This course introduces foundational concepts and methods in machine learning, emphasizing supervised and unsupervised learning, regression, and classification. It equips students with practical skills in data preprocessing, algorithm development, and neural networks. The course also provides exposure to handling real-world datasets and understanding clustering and dimensionality reduction techniques.

Introduction to Machine Learning

Objectives of machine learning, human learning vs machine learning, types of machine learning: supervised learning, unsupervised learning, regression, classification. The machine learning process: data collection and preparation, feature selection, algorithm choice, parameter and model selection, training, evaluation, bias-variance tradeoff, underfitting and overfitting problems.
Activity Based Learning
• Explore machine learning applications in real-world scenarios, compare human learning vs machine learning through examples, simulate bias-variance tradeoff using sample datasets.

Data Preprocessing
Data quality, data preprocessing: data cleaning, handling missing data and noisy data, data integration, redundancy and correlation analysis, continuous and categorical variables. Data reduction: dimensionality reduction using Linear Discriminant Analysis and Principal Components Analysis.
Activity Based Learning
• Perform data cleaning on raw datasets, handle missing and noisy values, apply dimensionality reduction techniques such as PCA and LDA, analyze correlation between features in real datasets.

Supervised Learning and Unsupervised Learning
Linearly separable and non-linearly separable populations, logistic regression, support vector machines: kernels, risk and loss functions, support vector machine algorithm, multi-class classification, support vector regression. Introduction to clustering, partitioning methods: K-means algorithm, mean shift clustering, hierarchical clustering, clustering using Gaussian Mixture Models. Clustering highdimensional data: problems and challenges.
Activity Based Learning
• Build and evaluate logistic regression models, implement and visualize support vector machines with different kernels, develop classification and regression models using SVM.
• Implement K-means and mean shift clustering on real-world datasets, perform hierarchical clustering and visualize dendrograms, cluster using Gaussian Mixture Models, explore challenges in high-dimensional clustering.

Neural Networks
Multi-layer perceptron, backpropagation learning algorithm, neural network fundamentals, activation functions, types of loss function, radial basis function network. Optimization: gradient descent algorithm, stochastic gradient descent, one case study.
Activity Based Learning
• Develop a neural network model using a multi-layer perceptron, implement backpropagation and activation functions, apply optimization techniques like SGD, build and evaluate a neural network-based controller for a selected application.

Tasks:

T1: Develop a machine learning model using a public dataset available for an application of your choice.
T2: Carry out data preprocessing tasks such as handling missing/outliers, dimensionality reduction, etc., on the public raw datasets available.
T3: Perform the correlation analysis between the various inputs and outputs of a system.
T4: Carry out the clustering on real-world datasets using any two popular clustering algorithms and comment on their performance.
T5: Build a multilayer neural network-based controller for a simulated nonlinear process of your choice.

Weightage: Continuous Assessment: 40%, End Semester Examinations: 60%.

Assessment Methodology: Assignments (20%), Solution to application-oriented problems using software (20%), Solving of GATE questions (20%), Internal Examinations (40%).

REFERENCES:

1. Alpaydin, E. (2004). Introduction to machine learning (Adaptive Computation and Machine Learning). The MIT Press.
2. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer.
3. Marsland, S. (2009). Machine learning: An algorithmic perspective. CRC Press.
4. Runkler, T. A. (2016). Data analytics: Models and algorithms for intelligent data analysis (2nd ed.). Springer Vieweg.
5. Laurene Fausett, (1993). Fundamentals Of Neural Networks, Pearson India.

List of Open Source Software / Learning Websites:

• https://lecturenotes.in/materials/64801-machine-learning-for-engineeringandscience-applications
• https://nptel.ac.in/courses/106105152
• https://nptel.ac.in/courses/106106139
• https://nptel.ac.in/courses/106106202
• https://nptel.ac.in/courses/110101145
• https://nptel.ac.in/courses/106106139
• https://nptel.ac.in/courses/106106236
• https://nptel.ac.in/courses/