Basic Digital Marketing Course
In this course, students will gain an understanding of the use of cameras and projection models for completing image processing. Students will be introduced to various techniques, including filtering, edge detection, segmentation, and clustering.
This 10-week course is designed to open the doors for students who are interested in learning about the fundamental principles and important applications of computer vision. We will expose students to a number of real-world applications that are important to our daily lives. More importantly, we will guide students through a series of well designed projects such that they will get to implement a few interesting and cutting-edge computer vision algorithms.
What you will learn
This program is designed to enhance your existing machine learning and deep learning skills with the addition of computer vision theory and programming techniques. These computer vision skills can be applied to various applications such as image and video processing, autonomous vehicle navigation, medical diagnostics, smartphone apps, and much more. This program will not prepare you for a specific career or role, rather, it will grow your deep learning and computer vision expertise, and give you the skills you need to start applying computer vision techniques to real-world challenges and applications.
1. Mathematics: Knowledge of and ability to use calculus, analytical geometry, linear
algebra and probability theory.
2. Programming: Ability to program in Python. Python, Statistics, Machine Learning, & Deep Learning
3. Other Courses: There are no specific pre-requisite courses. In particular, courses in AI, Machine Learning, Deep Learning, Computer Vision and Image Processing are not required.
4. Entrance Exam: No exam will be given to assess pre-requisites. However, GPA may be used to screen students for preparedness.
Flexible Learning: Self-paced, so you can learn on the schedule that works best for you
3 Months at 10-15hrs/week
Course 1: Introduction to Computer Vision
Master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks. Background, requirements and issues, human vision
Course 2: Advanced Computer Vision and
Deep Learning Learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application.
Course 3: Object Tracking and Localization
Learn how to locate an object and track it over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight.
Course 4 : Image formation: geometry and photometry
Geometry, photometry (brightness and color), quantization, camera calibration
Course 5 : Image segmentation and Feature Extraction
Various methods of image segmentation, edge detection, object proposals, SIFT features
Course 6 : Multi-view Geometry
Shape from stereo and motion, feature matching, surface fitting, Active ranging
Course 7 : Object Recognition: Traditional Methods
HoG/SIFT features, Bayes classifiers, SVM classifiers
Course 8 : Introduction to Neural Networks
Artificial neural networks, loss functions, backpropagation and SGD, Batch Normalization
Course 9 : Object Recognition: Deep Learning Methods
Image classification, object detection and semantic segmentation, adversarial attacks. Various neural network architectures, visualization techniques.
Course 10 : Motion analysis and Activity Recognition
Motion detection and tracking, Inference of human activity from image sequences
Examples: Face recognition, Image grounding, Visual question answering
1. Real-world projects from industry experts
2. Custom Study Plans
3. Technical Mentor Support
4. Practical tips and industry best practices
5. Personal Career Services
6. Flexible learning program
7. Additional suggested resources to improve
8. 100% Job Assistance after completion of course