20600 - DEEP LEARNING FOR COMPUTER VISION

Good knowledge of both Python and R.

Data is everywhere and it can be of any kind. Students enrolled in the Business Analytics track of the MSc in Data Science and Business Analytics have learned how to analyze numerical and textual data. This course augments their knowledge by teaching them how to deal with and analyze images. Images play a major role in today's communication, whether is corporate and official one or just a series of posts on Social Media. The course introduces the building blocks of modern Computer Vision algorithms by providing the students with a set of practical skills which can be applied in both the industry and academia.

• Theoretical understanding of different deep learning model architectures.
• Software libraries for Computer Vision.
• Image classification.
• Object detection.
• Image segmentation.
• Versioning software systems like git.

• Describe the main characteristics of a Computer Vision dedicated neural network.
• Recognize limitations of a given deep learning model.
• Select the appropriate model for a given Computer Vision task.

• Apply Computer Vision methods to solve real-world problems.
• Analyze, compare, assess the performance, and select the appropriate algorithm.
• Use relevant software libraries such as OpenCV, Tensorflow, Keras.
• Design the appropriate workflow to correctly organize, implement, and later manage a Computer Vision project.
• Develop a proper local and remote repository hosted on dedicated servers like Github.

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Group assignments

In addition to face-to-face lectures, the learning experience of this course includes state-of-the-art model applications, hackaton competitions among enrolled students, and interactions with guest speakers from companies. Students are encouraged to actively participate to classes and interact with guest speakers in order to use their communication and interpersonal skills.

• One final group project representing 70% of final grade.
• One final written exam representing 30% of final grade.

• One final individual project representing 40% of final grade.
• One final written exam representing 60% of final grade.

• Slides provided by the instructors.
• Francois CHOLLET (edition by Manning Publications): Deep Learning with R.
• Francois CHOLLET (edition by Manning Publications): Deep Learning with Python.