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Since June 2020
Instructor since June 2020
Translated by GoogleSee original
IT Course Level 2 for Students in French Schools
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From 99.93 C$ /h
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The second year is oriented towards design and integration activities; the training allows students to confront more complex problems: information systems, real time, networks, distributed architectures, artificial intelligence, compilation, production management ...

The emphasis is on coordination within the group: work sometimes stops after the detailed design.
Extra information
Bring your computer
Location
location type icon
Online from France
About Me
Pedagogy approved for more than 30 years in the training of students, employees, job seekers, change assistance, upgrading in the largest French training companies: Bull Formation, Demos, Atos, Cegos, ...
Education
Doctorate in Deductive Databases at the University of Paris-Sud Computer Science Research Laboratory Bat 490 Orsay
Master's degree in computer science applied to management at the Institut Supérieur de Gestion
Experience / Qualifications
Doctorate in Deductive Databases at the University of Paris-Sud Computer Science Research Laboratory Bat 490 Orsay
Master's degree in computer science applied to management at the Institut Supérieur de Gestion
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
French
English
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
Are you going to continue your studies in France?
In the first year of computer science, we try to ensure that students master:
the techniques,
the tools,
basic methods: design methods, algorithms, languages, operating systems, components of computing machines, etc.
Read more
PROGRAMMING

C language
C ++ language
HTML language
CSS language
JavaScript language> NodeJs> JQuery> Ajax> Json
XML language: XPath, Xslt, Xlink, Fop, Sax, Dom,
Language Java, Struts, Spring, JSF, JEE, EJB
Scala language
Language PHP, ZendFramework, CodeIgniter, Symphony, Mantis, Testmaker,
Language COBOL 1 COBOL 2 COBOL Object MicroFocus Fujitsu PacBase
FORTRAN language
LISP language, PROLOG
UML language
SQL language

MODELIZATION

Object Design Patterns,
Python language, Web Scraping, Scrapy, Selenium, BeautifulSoup
BigData Hadoop, Spark, Hive, Oozie, Zookeeper, Pig, Flume,
Neo4J, Redis
Elasticsearch, Kibana, Logstash
DataScience R language

DATABASES or INFORMATION SYSTEM

Database Oracle, Mysql, Sybase, Ingres, Universe, Informix, Access

FRAMEWORKS

BPM: jBPM, Camunda
ETL: Talend, Datastage
ERP: Odoo
CMS: Drupal
CRM: SugarCRM
E-Commerce: Prestashop, Magento
BI Lumira Business Objects

DEVOPS

Sonar code review,
Maven, Gradle, Continuous Integration Jenkins, Archiva, Confluence, Jira
JUnit test
Read more
Show more
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Raouf
Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

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"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
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Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
- Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right).
- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
* Compilation of the document and observation of the result.
* Structuring: Use of `\section` and `\subsection`.

Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
- Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`.
- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

5. From hand to screen: AI at the service of LaTeX (30 min)

- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.
verified badge
Erik
This course is aimed at students and scientists who want to use artificial intelligence to support the writing of academic papers without losing control of the content or compromising academic integrity.

The goal is not to delegate writing to AI, but to learn how to consciously and systematically integrate it into the scientific workflow. Throughout the course, the path from initial idea to final text is explored step by step, always with a critical eye.

We begin with chat setup and formulating appropriate prompts. Good interaction with AI doesn't depend on "tricks," but on clear questions and a well-defined framework. We'll show you how to guide the dialogue to obtain useful and consistent results.

Next, we work on developing the text's structure. AI can help develop a solid and logical outline, which will serve as the basis for subsequent argumentation. We explain how to evaluate and adapt these proposals according to academic criteria.

A central section is dedicated to source research. We analyze how AI can support information retrieval and sorting, and why citation and reference verification is essential. Methods for detecting errors, inaccuracies, and nonexistent sources are presented.

The proper use of footnotes and comments is also addressed. It explains how to integrate references, clarifications, and annotations without sacrificing clarity or formal rigor.

The final phase of the course focuses on writing and revising the text. It demonstrates how to use AI to improve style, coherence, and linguistic precision, as well as to identify argumentative problems. The course concludes with the process of exporting and preparing the final document for submission.

The course is designed for academic work such as essays, coursework, and theses, and is suitable for students of any discipline. No prior technical knowledge is required, only a willingness to work thoughtfully and responsibly.
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