This course introduces students to the fundamental concepts, tools, and techniques of deep learning. The course follows a gradual, beginner-friendly progression using the Python programming language and open-source libraries such as NumPy, Pandas, Matplotlib, scikit-learn, and TensorFlow/Keras. Topics move step by step from Python-based data handling and the mathematical foundations of neural networks, through the design and training of feedforward, convolutional, and recurrent neural networks. Through weekly hands-on laboratory sessions and an incrementally built capstone project completed across the term, students gain practical experience in preparing data, designing models, training and evaluating them, and presenting a complete deep learning solution to a real-world classification or prediction problem.
- Teacher: Angelo Joaquin