ECE 4252/8803 – Fundamentals of Machine Learning (FunML)

An introduction to the fundamentals and applications of Machine Learning.

Textbook: Inprint. (will be published)

Prerequisite: ECE 2026 [min C] (or equivalent)

Prerequisites with concurrency: ECE 3077 [min C] or ISYE/MATH/CEE 3770 [min C] or MATH 3670 [min C]

Assignments: Assignments will have both analytical parts and a hands-on part. The hands-on parts are coding assignments and they will look like mini projects. Students are expected to have some background in Python. Students will utilize the AI Makerspace for their codes in this course. These hands-on assignments vary between adding to existing codes, writing codes from scratch, searching the literature for codes for a specific application, or summarizing papers in the literature.

Programming Language: We will utilize Python throughout the course. We will also utilize a library of Jupyter notebooks in Georgia Tech’s AI Makerspace and Google Colab.

• Overview
  • History of Pattern recognition, Development of an ANN
  • Types of Learning i.e., Supervised, Semi-supervised, Weakly supervised, Un-supervised
  • General features of a supervised learning system i.e. features, training/validation set, labels, model complexity and overfitting etc.
  • Simple overview of Optimization
• Classification
  • Discriminative vs Generative modeling
  • Algorithms: Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, SVM, ANN
  • Classification Performance Evaluation
    • Cross-Validation using k-fold, Confusion Matrix, Precision, Recall, and F1 score, ROC,
  • A set of hand-on exercises on Colab
• Regression:
  • Linear Regression
    • Linear Regression, Performance Measures
    • Cost Function
  • Polynomial Regression
    • High Degree Polynomial Regression
  • Regularized Linear Models
    • Lasso vs. Ridge Regularization (L1/L2 regularization)
    • Dealing with high dimensional feature space – PCA
  • A set of hand-on exercises on Colab
• Clustering
  • Introduction     
  • Proximity Measures     
  • Similarity vs. Dissimilarity
  • Distance Measures
  • Common Clustering Methods
    • k-Means, GMM, Mean-shift, Spectral Clustering
  • Evaluating Clustering Performance
  • Image Segmentation as a clustering problem
  • A set of hand-on exercises on Colab      
• Neural Networks
  • Introduction to Artificial Neural Network:
    • Artificial Neuron
    • PyTorch
  • Non-linearity, Activations, Losses         
  • ConvNets
    • convolutional layer, pooling, FC, training
  • Boosting, Bagging, Stacking
  • Transfer learning
  • Data augmentation
  • A set of hand-on exercises on Colab
• Autoencoders
  • Fully Connected autoencoders, Conv AE, VAE
  • A set of hand-on exercises on Colab
• Sequence Modeling
  • RNN
  • GRU and LSTMs
  • Word embedding, Attention
• Data Efficient Learning
  • Active Learning
  • Self-supervised Learning
  • Weakly supervised Learning
• Advanced Topics
  • Explainability (XAI)
  • Uncertainty estimation
  • Anomaly detection
  • Robustness in Neural Network