BME-310-davidlin-2026-08-15-07-06-00

Understand the probabilistic nature of machine learning models.

Understand common supervised learning algorithms, linear regression, logistic regression, support vector machines (SVM), and decision trees.

Lectures, homework

Understand basics of artificial neural networks (ANNs), neurons, activation functions, and the role of backpropagation.

Lectures, homework

Understand basics of unsupervised learning techniques, k-means clustering, Gaussian mixture model, dimensionality reduction methods.

Lectures, homework

Perform model performance evaluation, determine accuracy, precision, recall, F1 score, and the Receiver Operating Characteristic (ROC) curve.

Lectures, homework

Week 1

Course introduction and syllabus overview; basic ML terminology. Supervised, unsupervised, and reinforcement learning (HW1: Python scripting basics using AI tools.) Python and IDE installation; first steps; variables, data types, algebraic expressions; modules (installation/import); control structures; functions

Lecture, in-class activities

Quizzes

Week 2

Creating Python scripts; structured programming; procedural vs. object-oriented programming; responsible use of AI for scripting; verification & validation (V&V) of scripts.

Lecture, in-class activities

Quizzes

Week 3

Supervised ML: regression - linear regression, cost function, gradient descent; train/validation/test splits, cross-validation, bias–variance, overfitting; Ridge/Lasso regularization. Intro to feature engineering. Model evaluation— MAE/RMSE/ (HW2: Applying regression to data.)

Lecture, in-class activities, homework

Quizzes, homework

Week 4

Classification: logistic regression; scaling/standardization; probabilistic perspective; generalized linear models (GLMs); categorical encodings. Model evaluation—accuracy, precision, recall, specificity, confusion matrix, F1 score, ROC AUC, PR AUC.

Lecture, in-class activities

Quizzes

Week 5

Joint and conditional probabilities; Bayes’ theorem; generative vs. discriminative algorithms. Fundamental limits of predictions. ChatGPT – the idea.

Lecture, in-class activities, homework

Quizzes, homework

Week 6

Naïve Bayes; Gaussian Discriminant Analysis (GDA); imbalanced data strategies (resampling, class weights); probability calibration and threshold selection. (HW3: Probabilistic models for classification.)

Lecture, in-class activities

Exam

Week 7

Support Vector Machines (SVM): margins, hyperplanes, support vectors.

Lecture, in-class activities, homework

Quizzes, homework

Week 8

Kernel trick for nonlinear decision boundaries; regularization; applications of kernel methods in SVMs. (HW4: SVMs with and without kernels.)

Lecture, in-class activities

Quizzes

Week 9

Artificial Neural Networks (ANN): neuron/perceptron model; feedforward networks and layers.

Lecture, in-class activities, homework

Quizzes, homework

Week 10

Activation functions, loss functions, optimization, and backpropagation. (HW5: Build and train a simple ANN for classification or regression.)

Lecture, in-class activities

Quizzes

Week 11

Data preprocessing and cleanup—handling missing values, scaling, normalization; Exploratory Data Analysis (EDA).

Lecture, in-class activities, homewok

Quizzes, homework

Week 12

Unsupervised ML: clustering—k-means and Gaussian Mixture Models (GMM); visualizing clusters.

in-class activities, project

Lecture, in-class activities, project

Week 13

Dimensionality reduction with Principal Component Analysis (PCA); anomaly detection; data visualization; brief introduction to reinforcement learning (for further study).

in-class activities, project

Lecture, in-class activities, project

Week 14

Project Presentations

Project

Project

Week 15

Project Presentations

Project

Project

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