01^st to 29^th December 2025

           IIT Kanpur Certificate Program on PYTHON
for Artificial Intelligence Machine Learning and Deep Learning   

      Organized by Prof. Aditya K. Jagannatham, EE Department, IIT Kanpur

Below is the detailed day-to-day & slot-wise program with Lecture Modules & Hands-On Coding Sessions for the IIT Kanpur Certificate Program on PYTHON for Artificial Intelligence Machine Learning and Deep Learning from 1^st to 29^th December 2025

	1st December, 2025
12:00 PM - 12:30 PM	Zoom Test Session
	Week-1
	3rd December, 2025
06:00 PM - 7:30 PM	Lecture 1: Introduction to Artificial Intelligence (AI) Machine Learning (ML)  •   Overview of AI, ML  •   Regression  •   Classification  •   Supervised Learning  •   Unsupervised Learning  •   Deep Learning  •   Test-Train Split  •   Metrics
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 2: Linear Algebra for AIML  •   Vector Representation  •   Inner Product  •   Orthogonality  •   Matrices  •   Matrix Inversion
	4th December, 2025
6:00 PM - 7:30 PM	Lecture 3: Linear Regression for AIML  •   Regression Model  •   Multiple Regressors  •   Model Computation  •   Pseudo inverse  •   Online Learning
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Project 1: IRIS Dataset Regression using PYTHON  •   IRIS Dataset Features  •   Linear Regression Module  •   Mean Squared Error (MSE)  •   R2 Score
	5th December, 2025
06:00 PM - 07:30 PM	Project 2: Boston Housing Price Analysis using PYTHON-Based Regression  •   Boston Housing set Features  •   Model Fitting  •   Model Performance  •   MSE, R2 Score  •   Regression Plot
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 4: Logistic Regression-Based AIML  •   Logistic Function  •   Class Probabilities  •   Likelihood and ML  •   Logistic Regression Metrics  •   Confusion Matrix
	Week-2
	8th December, 2025
06:00 PM - 7:30 PM	Project 3: SCIKIT Package for Logistic Regression using Purchase/ Shopping Data  •   Purchase/ Shopping Dataset Features  •   Logistic Model Fitting  •   Confusion Matrix Display  •   Accuracy Score
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 5: Support Vector Classifier (SVC) for Machine Learning  •   SVM Structure  •   Maximum Margin Classifier  •   Convexity and Convex Optimization  •   Kernel SVM
	9th December, 2025
06:00 PM - 7:30 PM	Project 4: IRIS Data Set classification using PYTHON-Based SVC  •   Dataset Features  •   Accuracy Metrics  •   Performance Evaluation
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 6: Naïve Bayes Technique for AIML  •   Feature Vector  •   Likelihood and Prior Probabilities  •   Naïve Bayes Principle  •   Posterior Probability Evaluation  •   Gaussian Naïve Bayes  •   Movie Recommender System Example
	10th December, 2025
06:00 PM - 7:30 PM	Project 5: Breast Cancer Dataset Analysis using SVC  •   Breast Cancer Dataset Features  •   SVM Fit  •   Gaussian Kernel  •   Polynomial Kernel  •   Sigmoid Kernel  •   Performance Assessment Project 6: Naïve Bayes Classification of Purchase Dataset  •   Purchase Dataset Features  •   Gaussian NB Model Fitting  •   Accuracy Metrics  •   Confusion Matrix Display
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 7: Discriminant Analysis (LDA) Based Data Classification  •   Gaussian Density  •   Multivariate Gaussian  •   Gaussian/ Linear Discriminant  •   Example Model Computation
	11th December, 2025
06:00 PM - 7:30 PM	Project 7: Discriminant Based Data Classification using IRIS Data Set  •   IRIS Dataset Features  •   LDA Model Fitting  •   Performance Visualization
7:30 PM-8:00 PM	Break
8:00 PM - 09:15 PM	Lecture 8: Data Clustering for Machine Learning  •   K-Means Algorithm  •   Centroid Computation  •   Cluster Assignment  •   Elbow Method for Number of Clusters  •   Silhouette Score
	12th December, 2025
	Break Day
	Week-3
	15th December, 2025
06:00 PM - 7:30 PM	Project 8: PYTHON Project for Data Clustering  •   K-Means Implementation  •   Elbow Curve  •   Silhouette Plot  •   Cluster Visualization
7:30 PM-8:00 PM	Break
8:00 PM - 09:15 PM	Lecture 9:Decision Tree Classifiers (DTC) for AIML  •   Optimal Feature Selection  •   Entropy  •   Conditional Entropy  •   Information Gain  •   Computation for Practical Restaurant Reservation Example
	16th December, 2025
06:00 PM - 7:30 PM	Project 9: Building a DTC for IRIS Dataset using PYTHON  •   IRIS Dataset Features  •   DTC Model Fitting  •   Accuracy Score  •   Confusion Matrix
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 10: Introduction to Neural Networks (NNs)  •   Neuron Structure and Properties  •   ANN Model  •   Activation Functions  •   One Hot Encoding  •   Categorical Crossentropy
	17th December, 2025
	Break Day
	18th December, 2025
06:00 PM - 7:30 PM	Project 10: Decision Tree Classifier using for Purchase Logistic Data Set  •   Purchase Dataset Features  •   DTC Visualization  •   DTC Prediction  •   Performance Metrics
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Leture 11: Deep Learning and Deep Neural Nets  •   Multi-layer Neural Networks  •   DNN Models  •   Dense and Sequential Architectures  •   NN Notation  •   Multi-layer Neural Nets  •   Gradient Descent  •   Backpropagation  •   Dropout Layers
	19th December, 2025
06:00 PM - 7:30 PM	Project 11: PYTHON-based Neural Network using PYTHON for Boston Housing Dataset  •   Boston Dataset Features  •   Sequential NN Model  •   Model Fitting  •   Training Epochs  •   Accuracy Performance
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Lecture 12: Convolutional Neural Networks (CNNs)  •   CNN Architectures  •   Convolution  •   Dot Product,  •   Padding  •   Hierarchical Structure  •   Max/ Average Pooling
	Week-4
	22nd December, 2025
06:00 PM - 7:30 PM	Test prep 1:Test preparation 1  •   Interview problem solving session  •   Solution discussion
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Project 12: Neural Network for analysis of Mobile Prices Dataset  •   Dataset Features  •   One Hot Encoding  •   Data Scaling  •   NN Model  •   Crossentropy  •   Adam Optimizer  •   Plots of Loss and Accuracy
	23rd December, 2025
06:00 PM - 7:30 PM	Project 13: Deep Learning for Fashion Classification using the MNIST Fashion Data Set  •   Fashion MNIST (Modified National Institute of Standards and Technology)      Dataset Description  •   MNIST Dataset Features and Classes  •   CNN Modeling  •   CNN Training  •   Sparse Categorical Crossentropy  •   Loss/ Accuracy Plotting Project 14: Deep Learning for Digit Classification using MNIST Digit DataSet  •   MNIST Handwritten Digit Dataset Features  •   CNN Architecture  •   Data Encoding  •   Softmax Activation  •   Confusion Matrix  •   Plots of Loss and Accuracy
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Distinguished Guest Lecture I: Dr. Dheeraj Nagaraj Research Scientist, Google PhD MIT
	24th December, 2025
06:00 PM - 7:30 PM	Project 15: Deep Learning using the CIFAR Dataset  •   CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes)  •   Dataset Visualization  •   CNN Architecture  •   Model Building and Compilation  •   Classification Accuracy  •   Confusion Matrix Metrics
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Distinguished Guest Lecture II: Dr. Sandeep Ponnuru ML and Computer Vision Engineer Santa Clara, California, United States
	25th December, 2025
	Break Day
	26th December, 2025
06:00 PM - 7:30 PM	Break
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Distinguished Guest Lecture III: Dr. Arjun Radhakrishna Principal Research Manager, Microsoft Redmond, Washington, United States
	Week-5
	29th December, 2025
06:00 PM - 07:30 PM	Project 16: IMDB Dataset and Deep Learning for Movie Rating Classification  •   Internet Movie Database (IMDb) Dataset Description  •   Vectorization  •   Binary Crossentropy  •   Model Training  •   Training and Validation Accuracy Plots  •   Training and Validation Loss Plots
7:30 PM-8:00 PM	Break
8:00 PM - 9:15 PM	Test prep 2: Test preparation 2  •   Interview problem solving session  •   Solution discussion