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        <dc:date>2026-03-02T22:51:32+03:00</dc:date>
        <title>Lab 01 - Plotting</title>
        <link>http://ocw.cs.pub.ro/courses/ep/labs/01?rev=1772484692&amp;do=diff</link>
        <description>Objectives

	*  Offer an introduction to Numpy &amp; matplotlib
	*  Get you familiarised with the numpy API
	*  Understand basic plotting with matplotlib

Python Scientific Computing Resources

In this lab, we will study a new library in python that offers fast, memory efficient manipulation of vectors, matrices and tensors: numpy. We will also study basic plotting of data using the most popular data visualization libraries in the python ecosystem: matplotlib.</description>
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        <dc:date>2025-02-12T00:00:12+03:00</dc:date>
        <title>Lab 02 - Advanced Plotting</title>
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        <description>Objectives

	*  Introduction to pandas
	*  Easy data manipulations with pandas
	*  Introduction to seaborn
	*  More types of cool looking plots with seaborn
	*  Apply what you learned on exploring COVID data for Romania

Resources

In this lab, we will study the basic API of pandas for easier data manipulations, and seaborn for some more advanced and visually appealing plots that are also easy to produce.</description>
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        <dc:date>2026-03-12T10:57:01+03:00</dc:date>
        <title>Lab 03 - CPU Monitoring</title>
        <link>http://ocw.cs.pub.ro/courses/ep/labs/03?rev=1773305821&amp;do=diff</link>
        <description>Objectives

	*  Offer an introduction to Performance Monitoring
	*  Present the main CPU metrics and how to interpret them
	*  Get you to use various tools for monitoring the performance of the CPU
	*  Familiarize you with the x86 Hardware Performance Counters
tasks index</description>
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        <dc:date>2026-03-12T10:57:13+03:00</dc:date>
        <title>Lab 04 - Memory Monitoring</title>
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        <description>Objectives

	*  Offer an introduction to Virtual Memory.
	*  Get you acquainted with relevant commands and their outputs for monitoring memory related aspects.
	*  Introduce the concept of page de-duplication.
	*  Present a step-by-step guide to Intel PIN for dynamic instrumentation.
tasks index</description>
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        <dc:date>2026-03-31T18:15:36+03:00</dc:date>
        <title>Lab 05 - I/O Monitoring</title>
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        <description>Objectives

	*  Offer an introduction to I/O monitoring.
	*  Get you acquainted with a few linux standard monitoring tools and their outputs, for monitoring the impact of the I/Os on the system.
	*  Gives an intuition to be able to compare two relatively similar systems, but I/O different.
tutorial index

tasks index</description>
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        <dc:date>2026-04-07T00:26:56+03:00</dc:date>
        <title>Lab 06 - Network Monitoring</title>
        <link>http://ocw.cs.pub.ro/courses/ep/labs/061?rev=1775510816&amp;do=diff</link>
        <description>Objectives

	*  Dive into the inner workings of previously studied traffic monitoring / filtering tools
	*  Discuss methods of path discovery
	*  Integrate eBPF tools in more complex profiling tasks

Contents

Tasks
tasks index


Tasks

01. [20p] Primer / Reminder


Pro tip #1man
neovim.bashrc
.zshrcmanneovim</description>
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        <dc:date>2025-02-12T15:23:01+03:00</dc:date>
        <title>Lab 07 - Profiling</title>
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        <description>Contents
tasks index


Tasks

01. [20p] Systemd boot analysis

systemd is one of the most popular choices of service managers that acts as the init (PID=1) process on Linux distributions. Although some distributions prefer simpler alternatives such as OpenRC for Alpine Linux and Gentoo or BusyBox for embedded environments, chances are that you are using systemd at this moment.</description>
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        <dc:date>2025-02-12T00:01:02+03:00</dc:date>
        <title>Lab 08 - TBD</title>
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        <description>Objectives

	*  TODO

Contents

TODO

Tasks

TODO</description>
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        <dc:date>2026-03-03T10:56:14+03:00</dc:date>
        <title>Lab 09 - Machine Learning</title>
        <link>http://ocw.cs.pub.ro/courses/ep/labs/09?rev=1772528174&amp;do=diff</link>
        <description>Objectives

	*  Understand basic concepts of machine learning
	*  Remember examples of real-world problems that can be solved with machine learning
	*  Learn the most common performance evaluation metrics for machine learning models
	*  Analyse the behaviour of typical machine learning algorithms using the most popular techniques
	*  Be able to compare multiple machine learning models</description>
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        <dc:date>2025-02-11T22:58:24+03:00</dc:date>
        <title>Lab 10 - Machine Learning Optimization</title>
        <link>http://ocw.cs.pub.ro/courses/ep/labs/10?rev=1739307504&amp;do=diff</link>
        <description>Objectives

	*  TODO

Resources

TODO

Tasks

Google Colab Notebook

TODO

Feedback

Please take a minute to fill in the  feedback form for this lab.</description>
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        <dc:date>2025-02-12T00:00:24+03:00</dc:date>
        <title>Lab 11 - Portfolio Presentations</title>
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        <description>Content

This lab is dedicated only to portfolio presentations. The distribution of students can be found in the  catalog, according to the following domains: Security, Systems, and Vision.

Slides must be uploaded to  Moodle, together with the related documents, in the form of a ZIP archive, by 09.01.2023, at 08:00 AM. Delays are not accepted!</description>
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        <dc:date>2025-02-12T00:00:30+03:00</dc:date>
        <title>Lab 12 - Portfolio Presentations</title>
        <link>http://ocw.cs.pub.ro/courses/ep/labs/12?rev=1739311230&amp;do=diff</link>
        <description>Content

This lab is dedicated only to portfolio presentations. The distribution of students can be found in the  catalog, according to the following domains: Security, Systems, and Vision.

Slides must be uploaded to  Moodle, together with the related documents, in the form of a ZIP archive, by 09.01.2023, at 08:00 AM. Delays are not accepted!</description>
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