courses:bev033dle:lectures [CourseWare Wiki]

Syllabus

Lecture/ Practice	Date	Topic	Lecturer	Materials	Notes	Feedback / discuss
1.	19.2	Recap: linear classifiers, linear regression, logistic regression, loss function, empirical risk minimisation, regularisation	BF	slides		thread
	20.2	(no lab)	-

2.	26.2	Artificial neurons, activation functions, network architectures; sidestep: stochastic neurons; sidestep: biological neurons	BF	slides		thread
2.	27.2	Lab1: generative vs. discriminative learning I	BF	task model	due 9.04

3.	4.3	Neural networks as classifiers, empirical risk minimisation, loss functions, model complexity and generalisation bounds; neural networks as nonlinear regression models	BF	slides		thread
3.	5.3	Seminar	BF	problems
		Reading: generalization bounds, VC dimension, large margin: (J. Mount), (V. V. V’yugin, Theorem 6.8)
4.	25.3	Backpropagation	AS	slides bbb mp4		thread
4.	26.3	Lab2: generative vs. discriminative learning II	BF	assignment template record 9:00	due 7.05	thread

5.	1.4	Stochastic Gradient Descent	AS	slides bbb mp4
5.	2.4	Seminar	AS	problems record
		Reading: Practical Recommendations for Gradient-Based Training Y. Bengio
6.	8.4	Convolutional Neural Networks	AS	slides bbb mp4
6.	9.4	No meeting – Friday schedule	-

7.	15.4	Training neural networks 0: project pipeline, data collection, training/validation/test set, model selection (architecture), overfitting, early stopping	BF	slides bbb mp4		thread
7.	16.4	Lab3: PyTorch, training pipeline	AS	assignment template	due 21.05

8.	22.4	Training neural networks 1: Data augmentation, Weight initialisation, Batch normalisation	BF	slides bbb mp4
8.	23.4	Seminar	AS	problems		thread

9.	29.4	Training neural networks 2: Adaptive SGD methods	AS	slides bbb mp4
9.	30.4	Lab4: Pretrained CNN Finetuning	AS	task	due 28.05
		Reading: Overview of GD Optimization, Mirror Descent
10.	6.5	Training neural networks 3: L2 weight regularization, dropout, and a bit beyond	AS	slides bbb mp4
10.	7.5	Seminar	AS	problems notes

11.	13.5	Training neural networks 4: adversarial patterns, robust learning approaches	BF	slides bbb mp4
11.	14.5	Lab5: Network visualisation, adversarial patterns	BF	assignment template record	due 11.06

12.	20.5	Generative models: VAE, GANs (introductory level)	BF	slides bbb mp4
12.	21.5	Lab6: Generative networks (optional/bonus lab)	BF	assignment bbb	submit before 18.6.

13	27.5	Supervised representation and similarity learning	Giorgos Tolias	slides bbb mp4
13	28.5	Seminar	BF	problems

14.	3.6	Recurrent neural networks: recurrent back-propagation, RNN, GRU, LSTM	BF	slides
14.	4.6	(no lab)