IC25401 Machine Learning and Deep Learning Syllabus:

IC25401 Machine Learning and Deep Learning Syllabus – Anna University UG Syllabus Regulation 2025

Course objective:

This course aims to provide a strong foundation in the fundamental concepts of machine learning and its core techniques. It introduces deep learning architectures such as CNNs, RNNs, and transfer learning for solving complex tasks in classification and sequence modeling.

Introduction to Machine Learning

Learning algorithms, Maximum likelihood estimation, Building machine learning algorithm, Neural Networks Multilayer Perceptron, Back-propagation algorithm and its variants Stochastic gradient decent, Curse of Dimensionality.
Activity Based Learning
Implement a simple machine learning model (e.g., linear regression or perceptron) using a dataset in Python.

Introduction To Deep Learning &Convolution Neural Networks

Machine Learning Vs. Deep Learning, Representation Learning, Width Vs. Depth of Neural Networks, Activation Functions: RELU, LRELU, ERELU, Unsupervised Training of Neural Networks, Restricted Boltzmann Machines, Auto Encoders. CNN Architectural Overview – Motivation – Layers – Filters – Parameter sharing – Regularization, Popular CNN Architectures: ResNet, AlexNet – Case studies.
Activity Based Learning
• Implement activation functions (RELU, LRELU, etc.) and visualize their outputs on sample data.
• Build a simple CNN for image classification

Sequence Modelling – Recurrent and Recursive Nets

Recurrent Neural Networks, Bidirectional RNNs – Encoder-decoder sequence to sequence architectures – BPTT for training RNN, Long Short Term Memory Networks – Case studies.
Activity based Learning
Build a basic RNN for text prediction

Auto Encoders and Deep Generative Models

Deep Belief networks – Boltzmann Machines – Deep Boltzmann Machine – Generative Adversial Networks – Case studies.
Activity based Learning
Visualize how an autoencoder compresses and reconstructs image data.

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

Assessment Methodology: Assignments (20%), Solution to application-oriented problems using software (20%), Projects based on open source data (like kaggle) (20%), Internal Examinations (40%).

References:

1. Alpaydin, E. (2014). Introduction to machine learning (3rd ed.). MIT Press, Prentice Hall of India.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2017). Deep learning. MIT Press.
3. Michelucci, U. (2018). Applied deep learning: A case-based approach to understanding deep neural networks. Apress.
4. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. The MIT Press.
5. Patterson, J., & Gibson, A. (2017). Deep learning: A practitioner’s approach. O’Reilly Media.
6. Zaccone, G., Karim, M. R., & Menshawy, A. (2017). Deep learning with TensorFlow: Explore neural networks with Python. Packt Publishing.

List of Open Source Software/ Learning website:

https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML
https://www.techtarget.com/searchenterpriseai/definition/deep-learning-deep-neuralnetwork
https://www.simplilearn.com/tutorials/deep-learning-tutorial/rnn
https://machinelearningmastery.com/what-are-generative-adversarial-networksgans/
1. https://nptel.ac.in/courses/106106139
2. https://nptel.ac.in/courses/106106236
3. https://nptel.ac.in/courses/106106202