Introduction to Neural Networks

Uitgebreide vaknaam	Introduction to Neural Networks
Leerdoelen	Upon successful completion of the course unit, students are able to*: 1. Describe the workings of simple linear classifiers, feed-forward neural networks, and simple recurrent NNs, in their own words and by means of the appropriate mathematical formulas 1.1; 2. Describe the workings of basic NN training techniques (such as stochastic gradient descent) 1.1; 3. Implement, train and test simple NN models of various kinds using a Python-based deep learning framework (e.g. Keras or PyTorch) 2.2, 2.3, 2.5; 4. Explain the conceptual differences between the abovementioned models and more advanced NN architectures (such as LSTMs) 1.1, 1.2; 5. Choose the right (family of) NN architectures to solve a given text classification problem 2.1, 2.2, 3.1; 6. Explain the implications/strengths/drawbacks of using deep learning versus other machine learning techniques in the context of NLP 3.1, 5.1, 5.2. *Numbers between brackets are taken from OER.
Omschrijving	Neural networks (NNs) form the basis of a large majority of contemporary Machine Learning solutions to information processing problems. Understanding the workings of NNs has therefore become an essential skill in both industrial and research environments. This course teaches you the fundamental notions of NNs: namely, how they work, how they differ from simpler (linear) models, how they can be trained. It explains a number of standard NN architectures, and gives you the instruments to understand more complex ones. On the practical side, this course will initiate you to the use of a Python-based deep learning framework for building and training NNs.
Uren per week	variabel
Onderwijsvorm	praktische oefening
Toetsvorm	schriftelijk tentamen
Vaksoort	bachelor
Coördinator	A. Bisazza, PhD.
Docent(en)	A. Bisazza, PhD. , student-assistent
Verplichte literatuur	Titel Auteur ISBN Prijs Slides from the Lectures “Speech and Language Processing” (3rd ed. draft) Freely available online: https://web.stanford.edu/~jurafsky/slp3/ James H. Martin Additional references to book chapters and/or articles may be provided during the lectures
Entreevoorwaarden	- Introduction to programming I: must have successfully passed the course; - Introduction to programming II: must have successfully passed the course; - Advanced Programming: must have attended lectures and performed satisfactorily on the practical assignments; - Project Text Analysis: must have attended lectures and performed satisfactorily on the practical assignments.
Opmerkingen
Opgenomen in	Opleiding Jaar Periode Type Course units for exchange students 3 semester I a verplicht BSc Informatiekunde 2 semester I a verplicht