It will be around 15 to 20 minute-presentation for each student.
try to be online ~20 minutes before the presentations
You already have your link for the meeting in one of the latest announcements.
==================
Ferhat Y.: DiffusionDet: Diffusion Model for Object Detection | 13.00 - 13.20
Abdullah C.: Zero-shot spatial layout conditioning for text-to-image diffusion models 13.20 - 13.40
Mustafa U.: R3D3 Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras 13.40 - 14.00
Orhan K: What do neural networks learn in image classification? A frequency shortcut perspective 14.00 - 14.20
Melike Nur Y.: Unmasking Anomalies in Road Scene Segmentation 14.20 - 14.40
Abdullah Ç.: Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color 14.40 - 15.00
Mehmet A.: Learning Human Dynamics in Autonomous Driving 15.00 - 15.20
Ahmad A.: Moment Detection in Long Tutorial Videos 15.20 15.40
Hayrettin E.: WaterMask Instance Segmentation for Underwater Imagery 15.40 - 16.00
Muhammed Tayyip K.: 3D Segmentation of Humans in Point Clouds with Synthetic Data 16.00 - 16.20
The final exam for KOM6110 ANN Machine Learning will be held
on June 3 2024 Monday between 10.40 - 11.50.
KOM6110 ANN_Machine_Learning Project Representation - Information
KOM6110 Presentations - Announcemen....txt
KOM6110 ANNMachineLearning Project Papers
Please refer to the link in the attached txt file for the project papers.
- Each student should pick a paper, study it, and present the background, implementation, and theory of the paper.
- Send me an e-mail (mercimek@yildiz.edu.tr), put a title like: "KOM6110 Project Paper - Your Name" and let me know the paper you may study. (The due date for sending the e-mail is 17/05/2024 midnight.
- If I approve the paper you selected, you can start studying it.
KOM6110 ANN_Machine_Learning Week 11 Information Theoretic L
KOM6110 ANN Machine Learning Week {1-7}
KOM6110 ANN_Machine_Learning Week 1 Introduction
KOM6110 ANN_Machine_Learning Week 2 Intro Knowledge-Learning
KOM6110 ANN_Machine_Learning Week 3 Perceptrons
KOM6110 ANN_Machine_Learning Week 4 Perceptrons II
KOM6110 ANN_Machine_Learning Week 5 Bayes
KOM6110 ANN_Machine_Learning Week 6 Bayes II
KOM6110 ANN_Machine_Learning Week 7 CNN deep learning
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# install Python 3.10.9 (or a newer version)
# on cmd.exe
python get-pip.py
#get-pip.py is under JupyterLabProjects folder
pip install opencv-python
pip install jupyterlab
python -m pip install -U matplotlib
# on cmd.exe type,
jupyter-lab
KOM6110 ANN_Machine_Learning Assg 1 Spring 2024
...... There is a correction regrading the 1st question.
1) ...
a) Create a
suitable perceptron (a single unit)
structure
b) Train it
with perceptron learning using Standard Approximation, and test it
with the test data.
c) Train it
with perceptron learning using Stochastic Approximation, and test
it with the test data.
d) Train it
with delta rule which uses gradient descent and Standard Approximation,
and test it with the test data.
e) Train it
with delta rule which uses gradient descent and Stochastic Approximation,
and test it with the test data
f) Get
related figures to display data distribution, classification for training data,
classification for test data, training
error vs iterations
(Stochastic
Approximation or Standard Approximation is
about the way you update the weights)
(refer to Week3, slides 13 and 14)