ITU-ACM-24-25-Machine-Learning-Course

11.12.2024 Wednesday 18.00 @ BBF Z-18

The course will be in Turkish.

Prerequisities

➡ Intermediate Python knowledge!

➡ Familiarity with Numpy and Pandas!

➡ Entry-level Calculus, Linear Algebra and Statistics knowledge!

➡ Wishing for a pleasant journey in the world of data is required!

Goals

Obtaining, organizing and visualizing data and creating a project with meaningful results. In doing so, utilizing machine learning concepts.

The students will:

• be able to bring complex datasets into a simple format.
• visualize data and draw meaningful conclusions.
• use basic machine learning concepts.
• have an idea about how Large Language Models work.

Syllabus

#	Topic	Instructor(s)	Time - Place
Lecture 1	Introduction and Basic Concepts	M. Tolga Kılınçkaya	11.12.2024 18:00
Lecture 2	Deep Learning & Optimization	M. Tolga Kılınçkaya	16.12.2024 18:00
Lecture 3	Regression	M. Tolga Kılınçkaya	18.12.2024 18:00
Lecture 4	Classification	M. Tolga Kılınçkaya	25.12.2024 18:00

Lectures will be around 1.5 hours. This course is 99% about Supervised Learning.

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Course Contents

Lecture 1 - Introduction and Basic Concepts

• What is Machine Learning?
• Supervised vs Unsupervised Learning
• Installation of Python and necessary libraries
• Data manipulation and visualization

Lecture 2 - Deep Learning & Optimization

• What is Deep Learing?
• Loss Funcitons
• Gradient Descent
• Activation Functions
• Backpropagation
• Evaluation

Lecture 3 - Regression

• L1 Loss & L2 Loss
• Linear Regression
• Regression Project

Lecture 4 - Classification

• Linear Classifiers
• Logistic Regression
• Classification Project

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Setup

Detailed setup instructions will be given during the first class.

• Jupyter Notebook with Python 3.10:

  pip install notebook
  

  to run:

  jupyter notebook
  

• Libraries:

   pip install numpy
  

   pip install pandas
  

   pip install tensorflow
  

   pip install matplotlib
  

   pip install scikit-learn