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CS 415 Introduction to deep learning

Fall 2026-2027 · 3 SU credits · 6 ECTS · Undergraduate

Not in the Spring 2026-2027 schedule — this is the most recent term it ran.

Introduction and artificial neural networks: their history, the perceptron and its limitations, boolean and real multi-layered perceptron, topologies, universal function approximation, decision boundaries, activations, sufficiency of architecture, Training I: recall on Gradient, Jacobian, Hessian; what is learning, empirical risk minimization, gradient descent, calculus of backpropagation, Training II: convergence issues, loss surfaces, momentum, optimization, second order methods, regularization strategies, initialization, Convnets I: definitions, types of convolutions, pooling, prominent architectures, Convnets II: vision models with convnets, feature pyramid, transposed convolution, object detection and segmentation, Sequence modeling I: Time series, RNNs, Sequence modeling II: Memory, LSTMs, sequence prediction, Attention: transformers, sequence to sequence predictions, LLMs and their downstream applications, Representation learning: autoencoders, self-supervision, unsupervised approaches, contrastive learning, Generative DL: Variational autoencoders, GANs and diffusion, Deep reinforcement learning: deep q-learning, Graph Neural networks, AI ethics

Needs first DSA 210 or CS 210

Alongside CS 415L

Lecture — CS 415

Lab — CS 415L

CS 415 — Sabancı Üniversitesi Fall 2026-2027 ders programı: section'lar, saatler, derslikler, CRN'ler ve syllabus bağlantıları.