Schedule - AIR.25
| Days | 1030-1300 Hrs (Lecture) |
1300-1430 Hrs | 1430-1700 Hrs (Hands-On) |
| MONDAY | Keynote Lecture and Robotic Lab Demonstration | L U N C H | Inauguration |
| TUESDAY | Fundamental of Deep Learning (DL) | Python Programming (Module-I) | |
| WEDNESDAY | Supervised Learning (Module-I) | Python Programming (Module-II) | |
| THURSDAY | Supervised Learning (Module-II) | Python Programming (Module-III) | |
| FRIDAY | Unsupervised Learning | Python Programming (Module-IV) | |
| SATURDAY | Introduction to ANN | Introduction to CNN | |
| SUNDAY | Introduction to RNN | Coding of ANN |
| Days | 1030-1300 Hrs (Lecture) |
1300-1430 Hrs | 1430-1700 Hrs (Hands-On) |
| MONDAY | CNN Coding | L U N C H | Natural Language Processing, Transformer Models |
| TUESDAY | Prompt Engineering | Semantic Search | |
| WEDNESDAY | Traditional Search with RAG | Hybrid Retrieval | |
| THURSDAY | Vector Databases and Indexing | Frontend and Interfaces | |
| FRIDAY | Agentic RAG, Local chatbot development | Introduction to Robotics | |
| SATURDAY | Introduction to ROS2 | Basic ROS Communication | |
| SUNDAY | URDF, TF2 | ROS Simulation |
| Days | 1030-1300 Hrs (Lecture) |
1300-1430 Hrs | 1430-1700 Hrs (Hands-On) |
| MONDAY | Locomotion, Waypoint Controller | L U N C H | Basic Navigation |
| TUESDAY | Simultaneous Localization and Mapping | Autonomous Navigation with Nav2 Stack | |
| WEDNESDAY | Motion Planning for Manipulators using MoveIt2 (Module I) | Motion Planning for Manipulators using MoveIt2 (Module II) | |
| THURSDAY | Robotics Hardware (Part-I) | Robotics Hardware (Part-II) | |
| FRIDAY | Robotics Hardware (Part-III) | Robotics Hardware (Part-IV) | |
| SATURDAY | Week Off | Week Off | |
| SUNDAY | Generative Adversial Network (Module-I) | Generative Adversial Network (Module-II) |
| Days | 1030-1300 Hrs (Lecture) |
1300-1430 Hrs | 1430-1700 Hrs (Hands-On) |
| MONDAY | Generative Adversial Network (Module-III) | L U N C H | Generative Adversial Network (Module-IV) |
| TUESDAY | Introduction to RL (Module-I) | Introduction to RL (Module-I) | |
| WEDNESDAY | RL Algorithm (Module I) | RL Algorithm (Module II) | |
| THURSDAY | RL Algorithm (Module III) | RL Algorithm (Module IV) | |
| FRIDAY | Certificate Distribution | Week Off | |
| SATURDAY | Week Off | Week Off | |
| SUNDAY | Week Off | Week Off |
| Days | 1700-1900 Hrs (Coding) |
Remark(s) |
| MONDAY | Define the Problem and Goal | The first step is to identify a real-world problem you want to address with ML. This could be anything from spam email detection to stock price prediction. Clearly define what your project aims to achieve. |
| TUESDAY | Data Collection | Machine learning thrives on data. You'll need to collect relevant data for your project. This could involve scraping data from the web, using public datasets, or generating your own data. Ensure the data is high-quality and aligns with your project's goal. |
| WEDNESDAY | Data Preprocessing | Raw data often needs cleaning and preparation before feeding it to an ML model. This might involve handling missing values, removing outliers, converting data types, and formatting the data for your chosen algorithms. |
| THURSDAY | Exploratory Data Analysis (EDA) | Get familiar with your data! Use Python libraries like pandas and matplotlib to explore the data's distribution, identify patterns, and uncover relationships between features. This helps you understand your data better and choose suitable ML techniques. |
| FRIDAY | Model Selection | There are various ML models available, each suited for specific tasks. Common choices include linear regression, decision trees, and neural networks. Consider the problem you're tackling and the characteristics of your data when selecting a model. |
| SATURDAY | Week Off | Not Applicable |
| SUNDAY | Week Off | Not Applicable |
| Days | 1700-1900 Hrs (Coding) |
Remark(s) |
| MONDAY | Model Training | Split your data into training and testing sets. The training set is used to train your model, while the testing set evaluates its performance on unseen data. Train your model using the chosen algorithm and the training data. |
| TUESDAY | Model Evaluation | Once trained, assess your model's performance on the testing set. Metrics like accuracy, precision, and recall help gauge how well your model generalizes to unseen data. |
| WEDNESDAY | Model Improvement | Evaluation might reveal areas for improvement. You can try tuning hyperparameters, adjusting the model architecture, or even exploring different algorithms altogether. |
| THURSDAY | Deployment | If your model performs well, consider deploying it for real-world use. This might involve integrating it into a web application, creating a standalone service, or even deploying it on mobile devices. |
| FRIDAY | Certificate Distribution (Online) | Hard Copy of the Certificate will be delivered by the post on your given corresponding address. |
| SATURDAY | Week Off | Not Applicable |
| SUNDAY | Week Off | Not Applicable |