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<!DOCTYPE html>
<html lang="en">
<head>
<title>SUT - Machine Learning - Fall 2023</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="description" content="Sharif University of Technology - Machine Learning Course - Fall 2023">
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<style>
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<body>
<div class="container mt-3">
<h2 style="text-align:center">Sharif University of Technology - Machine Learning Course - Fall 2023</h2>
<br>
<!-- Nav tabs -->
<ul class="nav nav-tabs" role="tablist">
<li class="nav-item">
<a class="nav-link active" data-bs-toggle="tab" href="#home">General Info</a>
</li>
<li class="nav-item">
<a class="nav-link" data-bs-toggle="tab" href="#notes">Lecture Notes</a>
</li>
<li class="nav-item">
<a class="nav-link" data-bs-toggle="tab" href="#assignments">Assignments</a>
</li>
<li class="nav-item">
<a class="nav-link" data-bs-toggle="tab" href="#teaching-assistant">Teaching Assistant Materials</a>
</li>
<li class="nav-item">
<a class="nav-link" data-bs-toggle="tab" href="#dates">Important Dates</a>
</li>
<li class="nav-item">
<a class="nav-link" data-bs-toggle="tab" href="#grading">Grading</a>
</li>
<li class="nav-item">
<a class="nav-link" data-bs-toggle="tab" href="#latest-news">Latest News</a>
</li>
</ul>
<!-- Tab panes -->
<div class="tab-content">
<div id="home" class="container tab-pane active"><br>
<p>The course is taught by <a href="https://scholar.google.com/citations?user=rJ-biB0AAAAJ&hl=en" target="_blank">Dr. Motahari</a> who is an assistant professor at the department of computer engineering.<br>
In this course, we will cover the following topics:
<ul>
<li>Introduction to Machine Learning and Statistical Learning</li>
<li>Statistical Learning</li>
<li>Classification</li>
<li>Resampling Methods</li>
<li>Linear Model Selection</li>
<li>Tree-based Methods</li>
<li>Support Vector Machines</li>
<li>Neural Networks and Deep Learning</li>
<li>Unsupervised Learning</li>
</ul>
Note: In this semester, we will put a huge focus on neural networks and deep learning. More precisely, you'll learn about the following concepts:<br><br>
<ul>
<li>Neurons and Perceptrons</li>
<li>Gradient Descent</li>
<li>Backpropagation</li>
<li>Feed-Forward Neural Networks</li>
<li>Recurrent Neural Networks (RNNs) including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)</li>
<li>Convolutional Neural Network (CNN)</li>
<li>AutoEncoders - VAEs</li>
<li>Generative Adversarial Networks (hopefully we'll have time for this!)</li>
</ul>
We have considered the following books as our main references:
<ul style="margin-top: 10px;">
<li><a href="https://hastie.su.domains/ISLP/ISLP_website.pdf" target="_blank">James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: With Applications in Python. New York: springer.</a></li>
<li><a href="https://hastie.su.domains/ElemStatLearn/" target="_blank">Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.</a></li>
</ul>
Useful Links:
<a href="https://quera.org/course/add_to_course/course/15199/" target="_blank">Quera (the password is "cesharifml")</a> | <a href="https://t.me/sutmlfall2023" target="_blank">Telegram Channel</a> | <a href="https://sharif-ml.github.io/past/spring-2023" target="_blank">Previous Semester</a>
</p>
</div>
<div id="notes" class="container tab-pane fade"><br>
<p>
<ul>
<li><a href="https://github.com/sharif-ml/sharif-ml.github.io/raw/main/notes/Note-1.pdf">Note 1</a></li>
<li><a href="https://github.com/sharif-ml/sharif-ml.github.io/raw/main/notes/Note-2.pdf">Note 2</a></li>
<li><a href="https://github.com/sharif-ml/sharif-ml.github.io/raw/main/notes/Note-3.pdf">Note 3</a></li>
</ul>
</p>
</div>
<div id="assignments" class="container tab-pane fade"><br>
<p>
<ul>
<li>Assignment 0: Make a <a href="https://kaggle.com" target="_blank">Kaggle</a> Contributor Profile</li>
<li><a href="https://github.com/sharif-ml/sharif-ml.github.io/blob/main/assignments/Assignment-1-Theory.pdf">Theoretical Assignment 1</a> | Due Date: 15/08/1402</li>
<li><a href="https://www.kaggle.com/code/malekzadeharman/ml-fall-2023-practical-1">Practical Assignment 1 | Linear Regression</a> | Due date: 19/08/1402</li>
<li><a href="https://github.com/sharif-ml/sharif-ml.github.io/blob/main/assignments/Assignment-2-Theory.pdf">Theoretical Assignment 2</a> | Due Date: 08/09/1402</li>
<li><a href="https://www.kaggle.com/code/malekzadeharman/ml-fall-2023-practical-2">Practical Assignment 2 | Naive Bayes</a> | Due date: 03/09/1402</li>
<li><a href="https://www.kaggle.com/code/malekzadeharman/ml-fall-2023-practical-3">Practical Assignment 3 | Logistic Regression</a> | Due date: 14/09/1402</li>
<li><a href="https://www.kaggle.com/code/malekzadeharman/ml-fall-2023-practical-4">Practical Assignment 4 | Support Vector Machine</a> | Due date: 26/09/1402</li>
<li><a href="https://github.com/sharif-ml/sharif-ml.github.io/blob/main/assignments/Assignment-3-Theory.pdf">Theoretical Assignment 3</a> | Due Date: 29/09/1402</li>
<li><a href="https://www.kaggle.com/code/malekzadeharman/ml-fall-2023-practical-5">Practical Assignment 5 | Decision Tree</a> | Due date: 12/10/1402</li>
</ul>
</p>
</div>
<div id="teaching-assistant" class="container tab-pane fade"><br>
<p>
<ul>
<li><a href="https://www.youtube.com/watch?v=8E9cicr24eg" target="_blank">Kaggle Tutorial: How to make a contributor profile</a></li>
<li><a href="https://www.youtube.com/watch?v=GtytJynJaLg" target="_blank">Kaggle API Tutorial</a></li>
<li><a href="https://www.youtube.com/watch?v=T6hWlPTvdVY" target="_blank">Virtual TA Session 1: Intro to Numpy and Pandas</a> | <a href="https://drive.google.com/file/d/1-IG-SWahmpoHiqL4Cfsc3sYCviztUdML/view?usp=sharing">Notebook</a></li>
<li><a href="https://www.youtube.com/watch?v=ce3u2Ah1u-8" target="_blank">Assignment Submission Tutorial</a></li>
<li><a href="https://www.youtube.com/watch?v=fHAgwCJ7wnk" target="_blank">Virtual TA Session 3: Intro to Neural Nets</a> | <a href="https://www.kaggle.com/code/malekzadeharman/introduction-to-neural-networks">Kaggle Notebook</a></li>
<li><a href="https://www.youtube.com/watch?v=gFFA8_WX5zI" target="_blank">Virtual TA Session 4: Neural Nets: LSTM</a> | <a href="https://www.kaggle.com/code/malekzadeharman/rnn-long-short-term-memory">Kaggle Notebook</a></li>
<li><a href="https://www.youtube.com/watch?v=tzTW-dHQmqY" target="_blank">Virtual TA Session 5: Neural Nets: CNN</a> | <a href="https://www.kaggle.com/code/malekzadeharman/cnn-intro">Kaggle Notebook</a></li>
<li><a href="https://www.youtube.com/watch?v=MPUd8SAT-8M" target="_blank">Virtual TA Session 9: Neural Nets: GRU</a> | <a href="https://www.kaggle.com/code/malekzadeharman/rnn-gated-recurrent-unit">Kaggle Notebook</a></li>
</ul>
</p>
</div>
<div id="dates" class="container tab-pane fade"><br>
<p>
<ul>
<li>Midterm Exam: 1402/09/25, from 16:00 to 19:00</li>
<li>Final Exam: 1402/11/02, 09:00 AM</li>
</ul>
</p>
</div>
<div id="grading" class="container tab-pane fade"><br>
<p>
<ul>
<li>Midterm: 6 Points + 1 (Bonus)</li>
<li>Final: 8 Points + 0.5 (Bonus)</li>
<li>Project: 2 Points</li>
<li>Assignments: 4 Points + 1 (Bonus) [7 Practical + 4 Theoretical Assignments] + 0.5 (conditional)</li>
<li>TA Class: 0.5 Points</li>
</ul>
</p>
</div>
<div id="latest-news" class="container tab-pane fade"><br>
<ul>
<li>The class will be held Saturdays and Mondays, from 9:00 AM to 10:30 AM at Ibn Sina Building, Alef 11.</li>
</ul>
</div>
</div>
</div>
</body>
</html>