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AI & Machine Learning: From Scratch to Advanced Models

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  1. The Course

    Meet the Instructors
  2. Introduction to the course
  3. Introduction
    What is Machine Learning?
  4. Universal Approximation Theorem
  5. Supervised Learning
  6. Unsupervised Learning
  7. Reinforcement Learning
  8. Historical Context and Evolution of Machine Learning
  9. Applications of Machine Learning in Naval Engineering
  10. Fundamental Concepts
    Overview
  11. Features, Labels, and Datasets
  12. Models
  13. The Machine Learning Pipeline
  14. Overfitting and Underfitting
  15. Core Algorithms
    Linear Regression
  16. Logistic Regression
  17. Decision Trees
  18. K-Nearest Neighbors (KNN)
  19. Support Vector Machines (SVM)
  20. Deep Learning Basics
    Introduction to Deep Learning
  21. Neural Networks: Structure and How They Work
  22. Neural Networks: Training
  23. What Patterns Do Networks Identify
  24. Gradient Descent
  25. Backpropagation
  26. Autodifferentiation: Simplified MLP
  27. Autodifferentiation: General MLP
  28. Autodifferentiation: Dimensionality
  29. Implementing a Simple Neural Network from Scratch: Loading the dataset
  30. Implementing a Simple Neural Network from Scratch: Polynomial fit
  31. Implementing a Simple Neural Network from Scratch: Parameter Initialization
  32. Implementing a Simple Neural Network from Scratch: Training and Results
  33. Data Handling and Preparation
    Understanding Data: Types of Data and Data Collection Methods
  34. Data Preprocessing: Cleaning and Normalization Techniques, Feature Engineering and Handling Missing Data
  35. Data Preprocessing: Practical Session
  36. Data Preprocessing Feature Engineering: Practical Session 2
  37. Exploratory Data Analysis: Visualizing Data, Identifying Patterns and Anomalies
  38. MLP Implementation with PyTorch
    Introduction to the Problem
  39. Setting Up the Environment and preprocessing data
  40. Explaining neural network with Python
  41. Building and training the model
  42. Evaluating and visualization results
  43. Conclusion
  44. Model Evaluation and Optimization
    Evaluation Metrics for Classification Models (Accuracy, Precision, Recall and F1 Score) and Confusion Matrix
  45. Evaluation Metrics for Regression Models (MAE, MSE, RMSE, R-score and MAPE)
  46. Evaluation Metrics for Deep Learning Models (Log loss, Cross Entropy Loss and Perplexity)
  47. Evaluation Metrics for Deep Learning Models: Practical Session
  48. Overfitting vs. Underfitting: Understanding Concepts and Techniques to Mitigate Overfitting
  49. Hyperparameter Tuning: Importance and Methods
  50. Hyperparameter Tuning: Practical Session
  51. Advanced Topics and Future Directions
    Introduction to Advanced Architectures: Convolutional Neural Networks (CNNs)
  52. Introduction to Advanced Architectures: Convolutional Neural Networks (CNNs) Practical Session
  53. Introduction to Advanced Architectures: Recurrent Neural Networks (RNNs)
  54. Introduction to Advanced Architectures: Recurrent Neural Networks (RNNs) Practical Session
  55. Future Trends in ML and Naval Engineering: Emerging Technologies and Potential Future Applications
  56. Ethical Considerations in AI: Use in Naval Engineering and Data Privacy and Security
  57. Final Assignment - Capstone Project
    Final Assignment (mandatory) (coming soon)
  58. Project Proposal and Dataset Selection
  59. Ship-D Dataset
  60. Analyze and Clean the Dataset
  61. Split the Dataset into Train, Validation, and Test
  62. Normalize the Data
  63. Define the Neural Network Architecture
  64. Train the Neural Network
  65. Test the Performance of the Neural Network
  66. Predict the Residuary Resistance for a given Hull
  67. Optional Extensions
  68. Course Materials
    Course Materials (coming soon)
  69. Course Survey
    Course Evaluation Survey (coming soon)
  70. Summary
    Wrap-up

Welcome!

We are excited to introduce you to the AI & Machine Learning: From Scratch to Advanced Models course, where you will go from understanding the fundamentals of AI to building and applying cutting-edge neural network architectures.

This course is hands-on and practical, ensuring that you not only grasp the concepts but also apply them in real-world scenarios. While many of our examples are from naval and ocean engineering, the principles and techniques are universal and relevant to any engineering or technical field. Plus, in the final project, you’ll have the opportunity to work on a dataset of your choice, making the learning experience truly personalized.

We’ll start by defining AI (Artificial Intelligence), Machine Learning, and Deep Learning, breaking down why these technologies are transforming industries and how they are shaping the future.

We’ll explore why machine learning works mathematically and the different types of learning: supervised, unsupervised, and reinforcement learning.

Once we have a strong foundation, we’ll move into key topics such as datasets, features and labels, model training, validation and testing, the machine learning pipeline, overfitting and underfitting, evaluation metrics, hyperparameter tuning and optimization, and model deployment. We’ll work through core machine learning algorithms like Linear and Logistic Regression, Decision trees and Random Forest, K-Nearest Neighbors, and Support Vector Machines.

Once the fundamentals are clear, we’ll dive into Deep Learning, breaking down how neural networks function, training using gradient descent, backpropagation and autodifferentiation, and how to interpret what a neural network learns.

To solidify your understanding, you’ll implement a neural network from scratch, predicting ship resistance based on hydrodynamic data using only NumPy and Pandas.

Handling data correctly is essential for training powerful models. We’ll teach you data types, collection methods, and pre-processing: cleaning, normalization and feature engineering, and exploratory data analysis techniques.

Then, we’ll transition to PyTorch, a widely used machine learning framework, and go through the entire machine learning pipeline from data preparation and model training to evaluation and optimization.

A great model is not just about building, it’s about fine-tuning for performance. We’ll cover evaluation metrics for classification and regression problems, handling overfitting and underfitting, and hyperparameter tuning techniques.

We won’t stop at basic neural networks: you’ll also explore convolutional neural networks for image processing, recurrent neural networks for sequential data analysis, and the latest trends and ethical considerations in AI and machine learning.

By the end, you will take on a capstone project where you can choose from a variety of datasets or work with the ShipD dataset from MIT to analyze 30,000 ship hull geometries and their wave resistance.

By the end of this course, you will have a deep understanding of how machine learning models work, be able to build, train, and evaluate your own AI models, have a working machine learning pipeline using real-world data, and be prepared to explore more advanced AI topics.

Whether you are an engineer, scientist, or AI enthusiast, this course will equip you with the skills to explore the immense ocean of possibilities that AI is unlocking.

Join us, and let’s shape the future together!

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