(update of conditions and grading) |
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| Riadok 43: | Riadok 43: | ||
|18:10 - 19:40 | |18:10 - 19:40 | ||
|H3 | |H3 | ||
| − | |[[ | + | |[[Iveta Beckova|Iveta Bečková]], [[Stefan Pocos|Štefan Pócoš]] |
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Verzia zo dňa a času 18:33, 20. február 2024
Neural Networks 2-AIN-132
Obsah
The aim of the course is to get acquainted with the basic concepts and algorithms of learning artificial neural networks and their use in solving various problems. The syllabus is a composition of older models with newer ones, in an effort to offer a balanced mosaic of approaches. Theoretical lectures are combined with practical modeling in Python exercises.
Schedule
| Type | Day | Time | Location | Teacher |
|---|---|---|---|---|
| Lecture | Wednesday | 9:50 - 11:20 | M-IV | Igor Farkaš |
| Exercise | Thursday | 18:10 - 19:40 | H3 | Iveta Bečková, Štefan Pócoš |
Syllabus
| No. | Date | Topic | References |
|---|---|---|---|
| 01 | 21.2. | Conditions for passing the course. Introduction, inspiration from neurobiology, brief history of NN, basic concepts. NN with logical neurons. | [U1/1][U3/1][U5/1] |
| 02 | 28.2. | Binary and continuous perceptron: supervised learning, error functions, binary classification and regression, linear separability. Relation to the Bayesian classifier. | [U1/1-3] |
| 03 | 06.3. | Supervised single-layer NS: Linear autoassociation: General Inverse model. Classification into n-classes. Error functions, relation to information theory. | [U4/3][U5/4] |
| 04 | 13.3. | Multilayer perceptron: error back-propagation algorithm. Training, validation, testing. Model selection. Bias-variance tradeoff. | [U1/4][U4/4] |
| 05 | 20.3. | Modifications of gradient methods, second-order optimization, regularization. Optimization problems. | [U1/15][U4/11] |
| 06 | 27.3. | Unsupervised learning, feature extraction, neural PCA model. Data visualization: self-organizing map (SOM) model. | [U1/8-9][U5/7] |
| 07 | 03.4. | Sequential data modeling: forward NS, relation to n-grams, partially and fully recurrent models, SRN model, BPTT, RTRL algorithm. | [U4/8][U5/6] |
| 08 | 10.4. | Hopfield model: deterministic dynamics, attractors, autoassociative memory, sketch of the stochastic model, modern versions. | [U1/13][U5/9] |
| 09 | 17.4. | Expansion of hidden representation: NS with radial basis functions (RBF), echo state network (ESN). | [U1/5][U2] |
| 10 | 24.4. | Deep learning. Convolutional neural networks: introduction. | [U3/6,9, U4/6] |
| 11 | 01.5. | More recent models: autoencoders, GRU, LSTM. | [U3/14,U4/9.1-2] |
| 12 | 08.5. | Stochastic recurrent models: basics of probability theory and statistical mechanics, Boltzmann machine, RBM model, Deep Belief Network. | [U1/11][U3/16] |
References
- Farkaš I. (2016). Neural networks. Knižničné a edičné centrum FMFI UK v Bratislave. Slajdes to the lectures (not updated).
- Haykin S. (2009). Neural Networks and Learning Machines (3rd ed.). Upper Saddle River, Pearson Education (k dispozícii na štúdium v knižnici FMFI, ale aj stiahnuteľné z webu). [U1]
- Jaeger H. (2007). Echo-state network. Scholarpedia, 2(9):2330. [U2]
- Goodfellow I., Bengio Y., Courville A. (2016). Deep Learning. MIT Press. [U3]
- Zhang A. et al. (2020). Dive into Deep Learning. An interactive deep learning book with code, math, and discussions, based on the NumPy interface. [U4]
- Kvasnička V., Beňušková., Pospíchal J., Farkaš I., Tiňo P. a Kráľ A. (1997). Úvod do teórie neurónových sietí. Iris: Bratislava. [U5]
Conditions and grading
- Submission of at least two (out of three) functioning projects during the semester (max. 3x10 = 30 points) and acquisition of at least 15 points in total. The deadlines will be announced on the webpage. The projects will offer bonuses (max. 4 points).
- Students may be asked to present the code during exercise as part of the submission.
- Each exercise (starting from the third one) will have a 5-minute exam, these will be worth 3x10 points. Another 10 points are awarded for active participation on the exercises. It is mandatory to obtain at least 20 points from exercises in total.
- Passing the final oral exam (3 questions, 30 points in total, pseudorandom choice). To register for the exam, you have to have at least two functioning projects graded. The exam is compulsory, you have to get at least 12 points.
- The lectures are not compulsory, but you can get up to 5 bonus points for participation.
- Overall grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).
Projects during the semester
- The project, together with the source code, is to be submitted before the deadline. Late submissions are penalized by -2 points each day. It is not possible to submit a project more than a week after the deadline.
- The projects are graded mainly based on content, but the form is considered, too (readability). The content should be comprehensible, i.e. graphical outputs combined with text.
- The model is to be implemented in Python and the project must be submitted as a PDF (no title page is required, the title and your name is enough).
- In case of plagiarism detection, the student automatically receives zero points from the project and will not be admitted to the exam.