Fixing 2023-24 tutorials pages

Author: Sergikavtaradze

our-initiatives/tutorials/2023-2024/index.mdx

@@ -8,18 +8,20 @@ import DocCardList from '@theme/DocCardList'
 
 # 💻 Machine Learning Tutorial Series
 
-Welcome to season 4 (2024-25) of the beginner machine learning tutorial series of the UCL Artificial Intelligence Society!
+Welcome to season 4 (2023-24) of the beginner machine learning tutorial series of the UCL Artificial Intelligence Society!
 
 If you have any questions about our content or machine learning more generally, feel free to ask us at the next session or make a forum post on the [UCLAIS Discord server](https://discord.gg/KSUZuQx?ltclid=3f704b3b-9044-415a-a2d7-e41007214187). You can also join our WhatsApp group chat through this [link](https://chat.whatsapp.com/JWEJn7OWvWE8MBfm2uSBhh).
 
 ## Our Team
 
 This academic year, the tutorial series is being delivered by the following people:
 
-- [Sergi Kavtaradze](#) (Head of Tutorials)
-- [Zachary Baker](#) (ML Officer)
-- [Paul Chaminieu](#) (ML Officer)
-- [Anna-Maria](#) (ML Officer)
+- [Angela Yu](#) (Head of Tutorials)
+- [Damien Bose](#) (ML Officer)
+- [Suhail Merali](#) (ML Officer)
+- [Arham Ali](#) (Tutorials Reviewer)
+- [Ilai Bachrach](#) (ML Officer)
+- [James Ray](#) (ML Officer)
 
 ## DOXA Challenges
 
@@ -42,22 +44,22 @@ During the first half term, we aim to cover basic concepts of **classical ML**:
 - Tutorial 0: **Introduction to AI**
 - Tutorial 1: **Introduction to Python**
 - Tutorial 2: **Regression**
-- Tutorial 3: **Classification 1**
-- Tutorial 4: **Classification 2** 
+- Tutorial 3: **Classification** (Doxa 1)
 
 After reading week, we will focus on **Deep Learning**!
 
-- Tutorial 4: **Neural Networks**
-- Tutorial 5: **Visual Computing I** (Doxa)
-- Tutorial 6: **Generative visual computing**
-- Tutorial 7: **Recurrent Neural Networks** (Doxa)
-- Tutorial 8: **Introduction to Transforments**
+- Tutorial 4: **Neural Networks** (Doxa 2)
+- Tutorial 5: **Visual Computing I**
+- Tutorial 6: **Visual Computing II**
+- Tutorial 7: **Recurrent Neural Networks**
+- Tutorial 8: **ClimateHack.AI Workshop**
 
 ### Term 2
 
 - Tutorial 9: **Natural Language Processing I**
 - Tutorial 10: **Natural Language Processing II**
-- Tutorial 11: **Graph neural networks / Reinforcement learning**
+- Tutorial 11: **Reinforcement Learning**
+- Tutorial 12: **ML Career Perspectives and Ongoing Learning Strategies**
 
 ## Previous Seasons
 
@@ -66,4 +68,4 @@ The content and resources from previous years are available on GitHub:
 - [Season 1 (2020/21)](https://github.com/UCLAIS/Machine-Learning-Tutorials) – led by [Danny Toeun Kim](https://github.com/kimdanny)
 - [Season 2 (2021/22)](https://github.com/UCLAIS/ML-Tutorials-Season-2) – led by [Martynas Pocius](https://github.com/MartynasPocius)
 - [Season 3 (2022/23)](https://github.com/UCLAIS/ml-tutorials-season-3) – led by [Filip Trhlík](https://trhlikfilip.com/)
-- [Season 4 (2023/24)](https://github.com/UCLAIS/ml-tutorials-season-4) – led by [Angela Yu](https://github.com/angela24680403)
+

our-initiatives/tutorials/2023-2024/intro-to-ai.md

@@ -4,12 +4,14 @@ sidebar_position: 2
 
 # 0: Introduction to AI
 
-**Date: 2nd October 2024**
+**Date: 11th October 2023**
 
 💡 We're excited to present our **Introduction to Artificial Intelligence** tutorial session! In this session, we'll take you on a journey to understand the different perspectives on AI and its evolution. We will also cover definitions of the key components of AI, including **Machine Learning** (ML) and **Deep Learning** (DL). **Generative AI**, especially models like GPT-3 and updated versions of this, has created significant attention in recent years. These models have demonstrated the ability to **generate human-like text and creative content** across multiple areas, ranging from **text to image and audio synthesis**. We will showcase some of these examples in the session! Please note that though this session is introducing what AI is in general, we will be mainly **focusing our series on ML**. 💡
 
-You can access our **demonstration notebook** here: 📘 [**Tutorial 0 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-0/ai_code_examples.ipynb)
+You can access our **demonstration notebook** here: 📘 [**Tutorial 0 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-0/ai_code_examples.ipynb)
 
 You can access our **slides** here: 💻 [**Tutorial 0 Slides**](https://www.canva.com/design/DAFm9tHNEDM/cEKEXWzmazR5KKN-7f06tw/edit)
 
-The **recording** from this session is available here: 🎤 [**Tutorial 0 Recording**](https://www.youtube.com/watch?v=OFS90-FX6pg)
+The **recording** from this session is available here: 🎤 [**Tutorial 0 Recording**](https://youtu.be/TzEdKvNFztI?si=3TVzoaWvdTi0U57-)
+
+(Apologies for the sound quality, you may need to turn it to max volume.)

our-initiatives/tutorials/2023-2024/intro-to-python.md

@@ -4,13 +4,13 @@ sidebar_position: 3
 
 # 1: Introduction to Python
 
-**Date: 9th October 2024**
+**Date: 18th October 2023**
 
 💡 **Python** is a common, **high-level programming language** known for its simplicity - this means that if you are new to coding, this language would be the perfect place to start! It has access to a lot of **libraries and frameworks**, including **NumPy**, **Pandas**, and **TensorFlow**, that are particularly useful for our ML series. In this session, the Tutorials team is collaborating with the Development team to create a workshop where we can provide direct help with **setting up your environment** and **walk you through Python exercises** we have prepared for you to get started! 💡
 
-You can access our **demonstration notebook** here: 📘 [**Tutorial 1 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_1_introduction_to_python.ipynb)
+You can access our **demonstration notebook** here: 📘 [**Tutorial 1 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-1/1_1_introduction_to_python.ipynb)
 
-Here are the **exercises** you can go through: 📘 [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_2_python_exercises.ipynb)
+Here are the **exercises** you can go through: 📘 [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-1/1_2_python_exercises.ipynb)
 
 You can access our **slides** here: 💻 [**Tutorial 1 Slides**](https://www.canva.com/design/DAFmvE-ptx0/lyY0SiOcjgSxrb201KcC8w/edit)