Deep Learning Course, 2026
This course provides a comprehensive exploration of modern deep learning techniques, from foundational concepts to advanced topics.
Introduction to Neural Networks: MLP, Backpropagation, Initialization, Optimization, Regularization, CNN
Natural Language Processing: Word Embeddings, RNN, LSTM, Attention, Transformer, LLM, Agents
Computer Vision: Classification, Object detection, Segmentation
Generative Models: Autoregression, VAE, GAN, Diffusion, Diffusion Models, Flow Matching, Multimodality
Advanced: Reinforcement Learning, Deep Learning Theory, Acceleration
Week #
Date
Topic
Lecture
Seminar
Recording
1
September, 8
MLP, Backpropagation
lecture
seminar
record
2
September, 15
Optimization, Regularization
lecture
seminar
record 2025
3
September, 22
Initialization, Normalization, CNN
lecture
seminar , notes
TBA
4
September, 29
Intro to NLP, Word Embeddings
lecture
seminar
record
5
October, 6
RNN, LSTM, Attention, Transformer
lecture
seminar
record
6
October, 13
LLM, Fine-Tuning, LoRA, RAG, Agents
lecture
NONE
record
7
October, 20
Classification, Object Detection
lecture
seminar
lecture , seminar
8
October, 27
Segmentation, Style transfer
lecture
seminar1 , seminar2
lectute , seminar
9
November, 3
Autoregression, VAE, GAN
lecture
seminar
record
10
November, 10
Diffusion Models, Flow Matching
lecture
seminar
record
11
November, 17
TBA
TBA
TBA
TBA
12
November, 24
TBA
TBA
TBA
TBA
13
December, 1
TBA
TBA
TBA
TBA
14
December, 8
TBA
TBA
TBA
TBA
14 Quizzes = 14 points
5 Homeworks = 50 points
Oral Exam = 40 points
Maximum Points: 14 + 50 + 40 = 104 points
Final Grade: min(round(#points/10), 10)
Probability Theory + Statistics
Machine Learning
Python