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## π» The Machine Learning Tutorial Series
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In the 2023/24 academic year, the UCL Artificial Intelligence Society [Machine Learning Tutorial Series](/our-initiatives/tutorials) enters its fourth season. If you are a beginner seeking to learn more about machine learning or just looking to consolidate your knowledge, join us as we cover different topics within the landscape of artificial intelligence. No prior machine learning experience is expected.
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In the 2024/25 academic year, the UCL Artificial Intelligence Society [Machine Learning Tutorial Series](/our-initiatives/tutorials) enters its fifth season. If you are a beginner seeking to learn more about machine learning or just looking to consolidate your knowledge, join us as we cover different topics within the landscape of artificial intelligence. No prior machine learning experience is expected.
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## 𧬠Nexus Labs
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The UCL Artificial Intelligence Society runs regular [journal club sessions](/our-initiatives/journal-club) at which leading researchers in both academia and industry share the latest developments in the world of artificial intelligence and machine learning.
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## π ClimateHack.AI 2023
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## π ClimateHack.AI 2024
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[ClimateHack.AI 2023](https://climatehack.ai/) is a second edition of a joint student initiative uniting the AI communities of 25 world-leading UK, US and Canadian universities to launch an ambitious AI competition to help cut carbon emissions in the energy sector by up to 100,000 tonnes a year! We are proud to host ClimateHack.AI 2023 on [DOXA](https://doxaai.com/) - an AI competition platform that originated in our society!
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[ClimateHack.AI 2024](https://climatehack.ai/) is a third edition of a joint student initiative uniting the AI communities of 25 world-leading UK, US and Canadian universities to launch an ambitious AI competition to help cut carbon emissions in the energy sector by up to 100,000 tonnes a year! We are proud to host ClimateHack.AI 2024 on [DOXA](https://doxaai.com/) - an AI competition platform that originated in our society!
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# π» Machine Learning Tutorial Series
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Welcome to season 4 (2023-24) of the beginner machine learning tutorial series of the UCL Artificial Intelligence Society!
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Welcome to season 4 (2024-25) of the beginner machine learning tutorial series of the UCL Artificial Intelligence Society!
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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).
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## Our Team
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This academic year, the tutorial series is being delivered by the following people:
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-[Angela Yu](#) (Head of Tutorials)
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-[Damien Bose](#) (ML Officer)
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-[Suhail Merali](#) (ML Officer)
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-[Arham Ali](#) (Tutorials Reviewer)
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-[Ilai Bachrach](#) (ML Officer)
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-[James Ray](#) (ML Officer)
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-[Sergi Kavtaradze](#) (Head of Tutorials)
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-[Zachary Baker](#) (ML Officer)
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-[Paul Chaminieu](#) (ML Officer)
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-[Anna-Maria](#) (ML Officer)
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## DOXA Challenges
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- Tutorial 0: **Introduction to AI**
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- Tutorial 1: **Introduction to Python**
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- Tutorial 2: **Regression**
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- Tutorial 3: **Classification** (Doxa 1)
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- Tutorial 3: **Classification 1**
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- Tutorial 4: **Classification 2**
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After reading week, we will focus on **Deep Learning**!
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# 0: Introduction to AI
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**Date: 11th October 2023**
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**Date: 2nd October 2024**
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π‘ 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**. π‘
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You can access our **demonstration notebook** here: π [**Tutorial 0 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-0/ai_code_examples.ipynb)
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You can access our **demonstration notebook** here: π [**Tutorial 0 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-0/ai_code_examples.ipynb)
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You can access our **slides** here: π» [**Tutorial 0 Slides**](https://www.canva.com/design/DAFm9tHNEDM/cEKEXWzmazR5KKN-7f06tw/edit)
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The **recording** from this session is available here: π€ [**Tutorial 0 Recording**](https://youtu.be/TzEdKvNFztI?si=3TVzoaWvdTi0U57-)
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(Apologies for the sound quality, you may need to turn it to max volume.)
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The **recording** from this session is available here: π€ [**Tutorial 0 Recording**](https://www.youtube.com/watch?v=OFS90-FX6pg)
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# 1: Introduction to Python
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**Date: 18th October 2023**
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**Date: 9th October 2024**
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π‘ **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! π‘
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You can access our **demonstration notebook** here: π [**Tutorial 1 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-1/1_1_introduction_to_python.ipynb)
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You can access our **demonstration notebook** here: π [**Tutorial 1 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_1_introduction_to_python.ipynb)
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Here are the **exercises** you can go through: π [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-1/1_2_python_exercises.ipynb)
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Here are the **exercises** you can go through: π [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_2_python_exercises.ipynb)
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You can access our **slides** here: π» [**Tutorial 1 Slides**](https://www.canva.com/design/DAFmvE-ptx0/lyY0SiOcjgSxrb201KcC8w/edit)
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