- What is the object detection problem?
- What is Intersection Over Union?
- How do you compare performance of multiple detectors?
- What are some of the detectors?
- What are sliding window detectors?
- What are some challenges with sliding window detectors?
- What is a Histogram of Gradients detector?
- What is Viola-Jones face detector?
- What are attentional cascades in neural networks?
- What is region-based convolutional neural network?
- Why from R-CNN to Fast R-CNN?
- What is Faster R-CNN?
- What is Region-based fully-convolutional network?
- What is You Only Look Once (YOLO)?
- How does 3D Object Detection work?

- What is face recognition?
- What is face verification evaluation protocol?
- How is face verification solved?
- What is a triplet loss training scheme?
- What is the re-identification problem?
- What are some challenges in person re-identification?
- What are some approaches to person re-identification?
- What is facial key-point regression?
- What are some approaches to key-point regression tasks?
- How to build a statistical model of facial shape?

- What is content based image retrieval?
- How do you define image similarity?
- How do you evaluate a image retrieval method?
- Computing semantic image embeddings using convolutional neural networks
- What are come of the first content based image retrieval systems?
- How does HOG descriptor work?
- What is a GIST descriptor?
- How can we use convolutional neural networks for image retrieval?
- What is a compact neural descriptor?
- How does vector quantization for image retrieval work?
- Hashing for image quantization
- How does Locality Sensitive Hashing works?
- What are the strengths and weaknesses of K-means and LSH?
- GIST IS for indexing large image collections.

- 03.00 - Probability and Information Theory
- 03.01 - Why Probability?
- 03.02 - Random Variables
- 03.03 - Probability Distributions
- 03.04 - Marginal Probability
- 03.05 - Conditional Probability
- 03.06 - The Chain Rule of Conditional Probabilities
- 03.07 - Independence and Conditional Independence
- 03.08 - Expectation, Variance and Covariance
- 03.09 - Common Probability Distributions
- 03.10 - Useful Properties of Common Functions
- 03.11 - Bayesâ€™ Rule
- 03.12 - Technical Details of Continuous Variables
- 03.13 - Information Theory
- 03.14 - Structured Probabilistic Models

- 30 Jun 2019 Computer Vision: Object Detection
- 23 Jun 2019 Deep Learning With TF 2.0: 04.00- Numerical Computation
- 16 Jun 2019 Computer Vision: Face Recognition
- 09 Jun 2019 Computer Vision: Image Retrieval
- 02 Jun 2019 Deep Learning With TF 2.0: 03.00- Probability and Information Theory