Artificial Intelligence using Python is a 30 Hours of Hands On Value added course. This course is for FET Students only, register for the course. Course will be from 18-22 March 2024.


Value Added Course (For 2024-25 Even Semester)

Continuous Internal Assessment  CIA Total    ESE
Theory and Hands On
VCE-W101AI Using Python2010201030701000

Note:  The course will commence in March/April, 2024 . End Semester / Internal will be conducted as regular course.

Course Name: AI Using Python

MM: 100 Time: 3 Hr. L   T  P 2    0  1Sessional: 30 ESE: 70 Credit : 0
Prerequisites:Understanding ofBasic Programming Concept and Mathematics (probability and statistics).
Objectives:Understand AI fundamentals and implement them using Python.Develop practical skills in AI through hands-on Python programming.Explore advanced AI topics and implement them using Python libraries.Apply AI techniques to real-world problems using Python. Cultivate collaboration and communication skills for AI projects using Python.
Course CoordinatorProf. Mayank Aggarwal
UnitModuleCourse ContentNo. of HoursPOs mappedPSOs mapped
Unit-1Module-1Introduction to AI and PyTorch Fundamentals: Introduction to AI: (Definition, history, and applications, AI vs ML, Different types of AI: Supervised, Unsupervised, Discriminative, Generative Core Ideas), Introduction to Tensors, Creating Tensors, Accessing Data from Tensors, Manipulating Tensors, Handling Tensor Shapes, Indexing on Tensors, Interoperability between PyTorch and NumPy, Ensuring Reproducibility, Utilizing GPU for Tensor Operations.06PO1/PO2/PO3PSO1/PSO2
Unit-2Module-2PyTorch Workflow fundamentals: Getting data ready, learning about linear regression, building a model, Fitting the model to data (training), Making predictions and evaluating a model (inference), Saving and loading a model, Putting it all together.06PO1/PO2/PO3PSO1/PSO2
Unit-3Module-3PyTorch Neural Networks Classification: Architecture of a classification neural network, Getting binary classification data ready, Building a PyTorch classification model, Fitting the model to data (training), Making predictions and evaluating a model (inference), Improving a model (from a model perspective), Non-linearity, Replicating non-linear functions, Putting it all together with multi-class classification.06PO1/PO2/PO3PSO1/PSO2
Unit-4Module-4Computer Vision and Custom Dataset: Model 0: Building a baseline model, Model 1: Adding non-linearity, Model 2: Convolutional Neural Network (CNN), Evaluating/Comparing our models, Transformation of Data, Model 0: TinyVGG without data augmentation, Exploring loss curves, Model 1: TinyVGG with data augmentation06PO1/PO2/PO3/PO4PSO1/PSO2
Unit-5Module-5Transfer Learning, Going Modular, Experiment Tracking, Model Deployment: Get and customize a pretrained model, View our model’s results in TensorBoard, Creating a helper function to track experiments, View modeling experiments in TensorBoard, Learn Machine Learning Model Deployment, Ethics and concerns in AI06PO1/PO2/PO3/PO4PSO1/PSO2
 Total No. of Hours30  
 Course OutcomesBlooms Level
CO1Understand AI history, applications, and types; grasp PyTorch basics including tensors, data manipulation, and GPU utilization.Remember L1  
CO2Master PyTorch workflow from data preparation to model fitting, evaluation, and deployment.Understand L2
CO3Implement neural network classification in PyTorch, handling non-linearity and multi-class scenarios.Apply L3
CO4Apply computer vision techniques with custom datasets, including model evaluation and transfer learning.Analyze L4
CO5Utilize pretrained models, experiment tracking, and ethical considerations in AI deployment.Create L5

Suggested books:

S. No.Name of Authors /Books /Publisher/Year
1.Artificial Intelligence with Python, Prateek Joshi., Packt Publishing Limited, 2017.
2.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurelien Geron, O’Reilly, 2017.
3.Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, Jeremy Howard and Sylvain Gugger, O’Reilly, 2020.
4.Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools, Eli Stevens, Luca Antiga and Thomas Viehmann, Manning Publishing Limited, 2020
5.The elements of statistical learning, Friedman, Springer series in statistics, 2001.
Program Outcomes (POs)Program Specific Outcomes (PSOs)
CO13 2 2 2        21
CO2221        12
CO3221         11
CO43232        33
CO5321         22