Neural Networks

Course description Neural Networks
Year: 2018-2019
Catalog number: 4343NEUNW
Teacher(s):
  • dr. W. Kowalczyk
Language: English
Blackboard: Yes
EC: 6.0
Level: 500
Period: Semester 2, Block III, IV
Hours of study: 26:00 hrs
  • Yes Elective choice
  • No Contractonderwijs
  • Yes Exchange
  • Yes Study Abroad
  • No Evening course
  • No A la Carte
  • No Honours Class

Admission requirements

Elementary calculus and linear algebra; basics of probability theory and statistics. Fluency in Python; basic commands of Linux.

Description

The course provides an introduction to key concepts and algorithms for neural networks, with strong emphasis on Deep Learning and its applications. It covers the following topics:
Part One: Classical Neural Networks
• Basics of statistical pattern recognition
• Linear models: Perceptron, Logistic Regression, Support Vector Machines
• Multi-layer Perceptron and Backpropagation
Part Two: DeepLearning
• Convolutional Networks
• Recurrent Neural Networks, LSTM and GRU Networks
• Reinforcement Learning, DNQ learning
• Autoencoders
• Restricted Boltzmann Machines
• Algorithms for training Deep Networks: SGD, Initialization, Batch Normalization, Dropout
• Software and hardware for Deep Learning
Moreover, several state-of-the-art applications of Deep Learning to image recognition, computer vision, language modeling, game playing programs, will be discussed. The course consists of weekly lectures, a few programming assignments (in Python) and the final written exam.

Course objectives

TThe objective of this course are:
• to provide a general introduction to the field of deep neural networks and their applications
• to develop practical skills for designing and training neural networks for tasks like image classification, speech recognition, forecasting, game playing
• to learn some popular tools for training deep architectures: Theano, TensorFlow and Keras

Timetable

The most recent timetable can be found at the students' website

Mode of instruction

  • Lectures
  • Computer Lab
  • Practical Assignments

Assessment method

The final grade will be the weighted average of grades for:

  • programming assignments (60%)
  • written exam (40%

Blackboard

See this Blackboard

Reading list

Deep Learning, by Yoshua Bengio, Ian Goodfellow, Aaron Courville
(available from http://www.deeplearningbook.org/)

Registration

  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

Contact information

Lecturer: dr. Wojtek Kowalczyk

Languages