Course - Advanced Deep Learning with Python - IE600320
IE600320 - Advanced Deep Learning with Python
Lessons are not given in the academic year 2022/2023
Examination arrangement: Portfolio
Grade: Passed / Not Passed
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This course has the aim of providing the foundations of deep learning with the most popular python libraries such as Sklearn, Keras and PyTorch. You will learn the key concepts underlying deep learning and how to use Python to develop machine learning pipelines to tackle real world problems using images, videos, text and time-series. This course will cover convolutional neural networks (CNN), recurrent neural networks (RNN), various advanced CNN architectures, and transfer learning.
Upon Completion of This Course, You'll Have:
- An understanding of the capabilities and limitations of machine learning (ML), and the knowledge of how to formulate your problem to solve it effectively.
- An understanding of convolution neural nets, recurrent neural nets, and state-of-the-art transfer learning models.
- An effective process for developing your machine learning pipeline to tackle real world problems such as machine vision, text understanding and time series prediction.
- The skills required to deploying, monitoring, and evaluating the ML model, as well as assessing its relevance, and the uses of different ML models.
- The basis required to collect, process, and utilize data efficiently.
- The basic skills required to select the right platform to deploy your model (cloud, edge device, hybrid) and how to configure it to achieve the required performance.
- The ability to document and communicate the results of your ML approach and guide your coding and ML efforts in the right direction.
Learning methods and activities
- The course will be offered in approximately 12 weeks (physically and digitally).
- Teaching approach: 5 hours each (lectures - practice - project work).
- Evaluation: exam will be in the form of a portfolio assessment where samples of work and mini projects will be used to evaluate the intended learning outcomes (ILOs) achievement throughout the course.
- Participants will get a certificate of course completion.
Further on evaluation
Portfolio assessment in terms of project(s) report and presentation. Apart from academic excellence, presentation skills are also important and will be evaluated. Ensure that the work submitted is clearly laid out and has legible figures, drawings, and diagrams. Report layout will be decided during the course. A basic report layout consists of an introduction where you summarize the state-of-the-art in this area and give a brief summary of your work. methodology, results, concluding remarks and a reference list. It is worth noting that it is a good habit of adding references wherever needed. The portfolio contains assignments that are carried out, digitally documented and submitted during the term. Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio. The final portfolio will be graded with a letter grade.
Admission to a programme of study is required:
Continuing Education, IIR (IEIIREVU)
Miscellaneous Courses - Faculty of Information Technology and Electrical Engineering (EMNE/IE)
Recommended previous knowledge
Basic knowledge of linear algebra and basic knowledge in Python.
A reading list will be continuedly updated before the start date of the course. To name a few:
- Josh Patterson and Adam Gibson (2017). Deep learning: a practitioner’s approach. O'Reilly.
- Andreas C. Muller and Sarah Guido (2017). Introduction to machine learning with python. O'Reilly.
Credits: 7.5 SP
Study level: Further education, lower degree level
Language of instruction: English
- Computer and Information Science
Department with academic responsibility
Department of ICT and Natural Sciences
Department with administrative responsibility
Centre for Continuing Education and Professional Development
Examination arrangement: Portfolio
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD Portfolio 100/100
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"