AI/ML Mini Course

ML Background (Day 1 / Hour 1)

Introduction & Key Tech Shifts

• Technological Eras

  • WWW / Internet: Enabled global connectivity.

  • Android / iOS / Mobile & Wearable: Brought computing to portable and wearable devices.

  • TensorFlow / PyTorch: Put machine learning frameworks into the hands of developers, making ML ubiquitous.

Programming Paradigms

• Empirical vs. Declarative vs. AI Programming

  • Empirical: Based on experimentation and observed data.

  • Declarative: Describes what outcome is desired, not how to compute it.

  • AI Programming: Systems learn from data rather than following explicitly programmed rules.

AI, ML, DL, and ANNs

1. Artificial Intelligence (AI)
   
   

   • Aim: Simulate human intelligence in machines.

   • Foundational Claim: Human intelligence can be precisely described and replicated by a machine.

2. Machine Learning (ML)
   
   

   • Definition: The study of algorithms that learn from data without explicit instructions.

   • Relationship to AI: ML is a subset of AI.

3. Deep Learning (DL)
   
   

   • Definition: Part of ML using artificial neural networks (ANNs) with multiple layers.

   • Learning modes: Supervised, semi-supervised, or unsupervised.

4. Artificial Neural Networks (ANN)
   
   

   • Loosely modeled on biological neurons (nodes/neurons connected by weights/biases).

Difference Between ML and AI

• ML: Focuses on building models from data for specific tasks.

• AI: Encompasses the broader goal of achieving intelligent behavior (ML is a major approach within AI).

Examples of ML & AI

• Image recognition, speech recognition, recommendation systems, predictive analytics, etc.

Understanding Deep Learning

• Layers (input, hidden, output)

• Nodes (neurons)

• Weights & Biases

• Activation Functions (ReLU, sigmoid, etc.)

Review of Key Terms

• Feature: Measurable property of data (input).

• Label: Desired output or classification category.

• Layer: A group of nodes in an ANN.

• Node: The computational unit (neuron).

• Training: Creating/learning the model from data.

• Inference: Using a trained model on new data.

• Regression: Predicts continuous values.

• Classification: Predicts discrete categories.

ANN Structure

• Nodes: Organized in layers.

• Weights: Connection strengths between layers.

• Biases: Offset terms in each node.

• Activation Functions: Transform outputs (e.g., ReLU, sigmoid).

Common Neural Network Types

• Deep Neural Networks (DNNs)

  • Typically feed-forward; data moves from input to output without cycles.

• Recurrent Neural Networks (RNNs)

  • Allow information to flow backward, useful for sequential data.

  • LSTM: A special RNN designed for long-term dependencies.

• Convolutional Neural Networks (CNNs)

  • Key for image processing and visual tasks.

Landscape of ML Methods

• Supervised Learning: Learning from labeled data (input-output pairs).

• Unsupervised Learning: Finding patterns in unlabeled data.

• Reinforcement Learning: Learning through rewards and penalties.

Introduction to Recommendation Systems

• Basic overview of how ML is used to recommend products or content.

Resources

• Machine Learning For Dummies®

ML Training (Day 2 / Hour 2)

How to Train a Machine Learning Model

• ML Creation & Configuration

  • Define model architecture and hyperparameters.

  • Prepare training data.

Important Hyperparameters

1. Learning Rate: How quickly weights are updated during optimization.

2. Epochs: Number of passes through the entire training dataset.

3. Batch Size: Number of samples processed before the model updates.

4. Activation Function: Function used to activate neurons (e.g., ReLU).

5. Hidden Layers: More layers → more complex model (increased expressiveness).

6. Weight Initialization: Often small random values.

7. Dropout: Randomly ignoring some neurons to prevent overfitting.

Learning & Optimization

• Softmax: Turns raw model outputs into probabilities.

• Backpropagation: Algorithm to compute gradients for weight updates.

• Gradient Descent: Minimizes loss by adjusting weights in the opposite direction of the gradient.

• Loss Functions: Quantify how far the model’s predictions are from desired outputs.

• Performance Metrics: Accuracy, precision, recall, F1-score, etc.

Data Handling

• Data Partitioning: Split into training, validation, and test sets.

• Cross Validation: Multiple train/validation splits to ensure robust performance estimates.

• Feature Engineering: Creating or transforming features (e.g., synthetic features).

• Overfitting: When the model learns noise in training data; mitigations include dropout, regularization.

Resources

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

• Python Data Science Handbook

• Machine Learning Crash Course (Google)

• Udacity Intro to TensorFlow for Deep Learning

Homework

• TensorFlow Exercises: Neural Network Playground (playground.tensorflow.org)

ML Prediction Engine (Day 3 / Hour 3)

Building a Basic ML Prediction Engine

• Focus on how a model makes inferences using trained parameters.

Gradient Computation & Differential Equations

• Partial Differential Equations (PDEs): Sometimes relevant in advanced ML for modeling.

• Numerical vs. Symbolic vs. Automatic Differentiation:

  • Numerical: Approximate derivatives from finite differences.

  • Symbolic: Manipulate mathematical expressions to get exact derivatives.

  • Automatic: Systematically applies chain rule to compute derivatives (used internally by ML frameworks).

Practical Steps

• Set up Colab or another environment (Python, TensorFlow).

• Walkthroughs:

  • Official TensorFlow fashion MNIST example

  • Clothing classification demos

Resources

• Python Data Science Handbook (GitHub)

ML & Fashion (Day 4 / Hour 4)

Applying ML to Fashion

• Fashion-Specific Challenges:

  • Attribute prediction (color, style, texture, etc.).

  • Trend forecasting.

  • Personalization and recommendation.

Competitions & Datasets

• iMaterialist Challenge

  • Focus: Recognizing product categories and attributes.

  • Tracks: Furniture/home goods & fashion (attribute prediction).

  • Dataset: Over 1 million fashion images labeled with various attributes.

Next Steps in ML & Fashion

• Combining deep learning with large-scale image datasets (like iMaterialist).

• Identifying innovative ways to automate design, production, and styling processes.