Welcome to my Substack. I am trying to write a series about the AI research roadmap. I will explain all the necessary fundamentals you need to know before becoming an AI researcher. My name is Sourena Khanzadeh, and I recently defended my PhD in computer science. Here are my two cents of advice for those who want to become AI researchers.

In this series, I will provide a deep dive into mathematics, research basics, and, finally, insights into state-of-the-art and classic research papers. Here is a list of things you need to know in order to become a fully fledged AI researcher, some of which you may already know.

One caveat: AI is a huge domain, and you must be specific about the topic you want to explore in order to become world-class in it. I am certainly not world-class in anything yet, but I am trying my best to get there. :-)

My Personal Roadmap

Mathematics

• Linear algebra
• Probability and statistics
• Information theory
• Optimization
• Calculus
• Coding

Python

• Syntax
• Basic keywords
• Basic built-in functions
• Conditionals
• Object-oriented programming

ML Frameworks

• PyTorch
• TensorFlow
• scikit-learn
• Other Libraries
• NumPy
• Matplotlib
• SciPy
• Seaborn

Machine Learning Fundamental's

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning
• Loss functions
• Regularization
• Overfitting and underfitting
• Cross-validation
• Bias–variance tradeoff
• Feature engineering
• Evaluation metrics

Deep Learning

• Neural networks
• Backpropagation
• Activation functions
• Initialization
• Normalization
• CNNs
• RNNs / LSTMs
• Transformers
• Attention mechanisms
• Autoencoders
• Diffusion models
• Generative models

Research Practice

• Read papers consistently
• Reproduce important papers
• Build small projects
• Run controlled experiments
• Write technical notes
• Share code publicly
• Learn to communicate results clearly
• Develop taste for good research questions
• Tools

Git and GitHub

• Linux / command line

• Jupyter notebooks

• Docker

• Conda / virtual environments

• Weights & Biases or TensorBoard

• Slurm or cluster computing basics

• Cloud platforms such as AWS, GCP, or Azure

• Fluency in English

• Reading

• Writing

Domains that you can specialize in after learning the basics are as follows:

• Computer vision
• Natural language processing
• Reinforcement learning
• Robotics
• Generative AI
• Multimodal learning
• AI safety
• Interpretability
• Causal inference
• Graph machine learning
• Recommendation systems
• AI for science
• Human-AI interaction
• Bias and fairness
• Privacy
• Security
• Model misuse
• Interpretability
• Alignment
• Evaluation of harmful outputs
• Responsible deployment

Next, we will explore the topic of linear algebra, as it is the backbone of machine learning. I will cover the necessary concepts and skills needed to understand vectors, matrices, and their operations, which form the foundation of modern AI systems.