Python Data Science Basics

June 24 - 28 | Morning

Students will jump into the basics of Data Science using Python. They will learn about Data Ingestion, Summarization, and Pre-processing all while learning to use Python Numpy and Pandas library. Then they will work on visualizing data with the Matplotlib library.

Technical Concepts

• Read Data files into Python usable datasets.
• Generate summarized data views including basic statistics for broad understanding of the dataset.
• Basic data visualization.
• Pre-process data for model building.

Artificial Intelligence & Machine Learning

Linear Regression & Classifications

July 15 - 19 | Morning

The week is designed for high school students interested in exploring the foundational concepts of artificial intelligence (AI) through supervised learning techniques. Over the course of the camp week, students will gain hands-on experience with linear regression and classification algorithms, enabling them to understand and apply these methods to real-world problems. Through a combination of theoretical lessons and practical exercises, participants will develop an understanding of how AI algorithms can be used to make predictions and classify data accurately.

Throughout the camp, students will engage in coding exercises using Python and popular libraries like scikit-learn to implement and experiment with these concepts, providing them with practical skills and a deeper understanding of AI in action.

Technical Concepts

• Supervised Learning: Introduction to the concept of supervised learning, where models learn from labeled data to make predictions or decisions.
• Linear Regression: Understanding linear regression as a statistical method used for modeling the relationship between a dependent variable and one or more independent variables. Concepts include:
  • Regression analysis
  • Ordinary least squares method
  • Evaluation metrics like mean squared error (MSE) and coefficient of determination (R-squared)
• Classification Algorithms: Exploring algorithms for classifying data into discrete categories. Key concepts include:
  • Logistic Regression: A classification algorithm used to model the probability of a categorical outcome.
  • Decision Trees: Tree-like models used to make decisions by splitting data based on feature values.
  • Support Vector Machines (SVM): Algorithms for finding the optimal hyperplane to separate data into different classes.
• Model Evaluation: Techniques for evaluating the performance of AI models, including:
  • Confusion matrix
  • Accuracy, precision, recall, and F1-score
  • Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC)
• Feature Engineering: Preprocessing techniques to improve model performance by selecting, transforming, or creating new features from the data.
• Applications: Real-world applications of linear regression and classification algorithms across various domains such as finance, healthcare, and marketing.

Artificial Intelligence & Machine Learning

Neural Networks & Deep Learning

July 29 - Aug 2 | Morning

The week is tailored for high school students eager to explore the cutting-edge realm of artificial intelligence (AI) through neural networks and deep learning. Throughout the week-long program, participants will dive into the fundamentals of neural networks, gaining experience in designing, training, and deploying deep learning models. By the end of the camp, students will be equipped with the knowledge and skills to tackle complex problems across various domains using AI techniques.

Participants will engage in interactive lectures, coding labs, and project-based activities, fostering a comprehensive understanding of neural networks and deep learning principles. By the end of the camp, students will be empowered to embark on their journey into the exciting field of AI with a solid foundation in deep learning techniques.

Technical Concepts

• Neural Networks Basics
  • Understanding the biological inspiration behind artificial neural networks.
  • Perceptron model and its architecture.
  • Activation functions (e.g., sigmoid, ReLU) and their role in neural networks.
• Feedforward Neural Networks
  • Structure and functionality of feedforward neural networks.
  • Forward propagation of data through neural network layers.
  • Loss functions (e.g., cross-entropy, mean squared error) for training neural networks.
• Deep Learning Architectures
  • Fully connected Neural Networks for image classification and recognition
  • Convolutional Neural Networks (CNNs) for image classification and recognition.
  • Recurrent Neural Networks (RNNs) for sequential data processing.
• Training Deep Learning Models
  • Data preprocessing techniques, including normalization and data augmentation.
  • Strategies for model validation and overfitting prevention (e.g., dropout, early stopping).
  • Hyperparameter tuning to optimize model performance.
• Deep Learning Applications: Real-world applications of deep learning across diverse domains, such as computer vision, natural language processing, and autonomous systems.
• Frameworks and Tools
  • Introduction to deep learning frameworks like TensorFlow.
  • Hands-on coding exercises using Python to implement neural networks and train deep learning models.

Artificial Intelligence & Machine Learning

Neural Networks, Deep Learning, and Basic Chatbots

Projects will be different from prior week, but will be more advanced. Attendance for the prior week is not necessary but recommended.

August 5 - 9 | Morning

Building upon previous knowledge, participants explore advanced deep learning techniques and venture into the realm of natural language processing (NLP) to create basic chatbots. They learn about recurrent neural networks (RNNs) and sequence-to-sequence models, applying them to develop conversational agents capable of understanding and generating human-like responses. By the end of the camp, students will not only understand the core concepts of AI and NLP but will also have practical experience in creating their own basic chatbots.

Technical Concepts

• Neural Networks and Deep Learning: Review of neural network architecture and activation functions. Review of deep learning fundamentals.
• Chatbot Development Basics
  • Overview of chatbot architecture and components.
  • Natural language understanding (NLU) techniques for extracting meaning from text.
  • Response generation using rule-based and retrieval-based methods.
• Chatbot Implementation and Deployment: Hands-on experience in building a basic chatbot using Python and NLP libraries.
• Evaluation and Improvement
  • Metrics for evaluating chatbot performance, including accuracy and user satisfaction.
  • Iterative improvement strategies through user feedback and model fine-tuning.