Schedule - AIR.24
| Days / Hours | 1030-1300 | 1300-1430 | 1430-1700 |
| WEDNESDAY | Lecture : Fundamental of Neural Network (NN) | L U N C H | Hands-On : Python Programming (Introduction, Expression Variable) |
| THURSDAY | Lecture : Fundamental of Deep learning | Hands-On : Python Programming (Functions, Conditions, Iterations) | |
| FRIDAY | Lecture : Fundamental of Convolution NN and Recurrent NN | Hands-On : Python Programming (Strings, Tuples, Lists, Dictionaries, Sets) | |
| SATURDAY | Doubt Clearing Session (On Demand) | Week Off | |
| SUNDAY | Week Off | Week Off | |
| MONDAY | Event Registration and Inauguration | Lecture & Hands-On : Data Preprocessing Techniques (Scaling, Missing Data, Encoding, etc.) | |
| TUESDAY | Lecture : Regression (Linear, Polynomial, Support Vector, Decision Tree, Random Forest) | Hands-On : Regression (Linear, Polynomial, Support Vector, Decision Tree, Random Forest) |
| Days / Hours | 1030-1300 | 1300-1430 | 1430-1700 |
| WEDNESDAY | Lecture : Classification (K Nearest Neighbour, Logistic Regression, Naïve Bayes) | L U N C H | Hands-On : Classification (K Nearest Neighbour, Logistic Regression, Naïve Bayes) |
| THURSDAY | Lecture : Classification (Support Vector Machine, Kernel SVM, Decision Trees, Random Forest, XGBoost) | Hands-On : Classification (Support Vector Machine, Kernel SVM, Decision Trees, Random Forest, XGBoost) | |
| FRIDAY | Lecture : Dimensionality Reduction (Principal Component Analysis, Linear Discriminant Analysis, Kernel PCA) | Hands-On : Dimensionality Reduction (Principal Component Analysis, Linear Discriminant Analysis, Kernel PCA) | |
| SATURDAY | Doubt Clearing Session (On Demand) | Week Off | |
| SUNDAY | Week Off | Week Off | |
| MONDAY | Lecture : Basics of Markov Decision processes, Dynamic Programming, Greedy algorithm | Hands-On : Dynamic Programming for Real World Application | |
| TUESDAY | Lecture : Q-Learning Algorithm, SARSA Algorithm | Hands-On : Implementation of Q-Learning Algorithm, SARSA Algorithm |
| Days / Hours | 1030-1300 | 1300-1430 | 1430-1700 |
| WEDNESDAY | Lecture : Deep Q-Learning Network (DQN) and Algorithm | L U N C H | Hands-On : Implementation of DQN Algorithm |
| THURSDAY | Lecture : Fundamental of Robot Motion Control | Hands-On : Robot Motion Control | |
| FRIDAY | Lecture : Robot Path Planning Algorithm | Hands-On : Robot Path Planning Algorithm | |
| SATURDAY | Doubt Clearing Session (On Demand) | Week Off | |
| SUNDAY | Week Off | Week Off | |
| MONDAY | Lecture : Introduction to Localization, Mapping, SLAM, Control | Lab Visit and Playing with CoppeliaSim (or) SLAM Libraries | |
| TUESDAY | Lecture : Configuration Space and Deliberative Planning Algorithms | Hands-On : Introduction to ROS |
| Days / Hours | 1030-1300 | 1300-1430 | 1430-1700 |
| WEDNESDAY | Lecture : Reactive Planning Algorithms | L U N C H | Hands-On : Playing with TurtleSim, Gazebo |
| THURSDAY | Lecture : Hybrid Planning Algorithms, Behavioral Programming of Robots | Hands-On : ROS Navigation Stack, Programming Multiple Robots | |
| FRIDAY | Certificate Distribution (30 Days Training) | Keynote Lecture | |
| SATURDAY | Doubt Clearing Session (On Demand) | Week Off | |
| SUNDAY | Week Off | Week Off | |
| MONDAY | Lecture : Speech Processing | Hands-On : Speech Processing | |
| TUESDAY | Lecture : Speech Processing | Hands-On : Speech Processing |
| Days / Hours | 1700-1900 | Remark(s) |
| WEDNESDAY | 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. |
| THURSDAY | 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. |
| FRIDAY | 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. |
| SATURDAY | Week Off | Not Applicable |
| SUNDAY | Week Off | Not Applicable |
| MONDAY | 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. |
| TUESDAY | 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. |
| Days / Hours | 1700-1900 | Remark(s) |
| WEDNESDAY | 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. |
| THURSDAY | 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. |
| FRIDAY | Model Improvement | Evaluation might reveal areas for improvement. You can try tuning hyperparameters, adjusting the model architecture, or even exploring different algorithms altogether. |
| SATURDAY | Week Off | Not Applicable |
| SUNDAY | Week Off | Not Applicable |
| MONDAY | 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. |
| TUESDAY | Certificate Distribution (Online) | Hard Copy of the Certificate will be delivered by the post on your given corresponding address. |