Topic	Content Overview	Resources
First Part
Python intro, JetBrains Academy plugin in PyCharm	1. Main goals of the AI club 2. Gentle intro to Python 3. Objects representation in Python 4. PyCharm, JetBrains Academy plugin	📺 Video | 📝 Assignment: Python practice
NumPy & Pandas	1. Numpy: intro and ndarray 2. Slices and Indexing 3. Concatenation and broadcasting 4. Pandas DataFrames, Titanic Dataset	📺 Video | 📝 Numpy practice
Intro to Machine Learning: tasks types, examples, quality evaluation	1. Types of ML tasks, math statement 2. Linear Regression in sklearn 3. Overfitting, train/test split 4. Significant events in machine learning history	📺 Video | 📝 Pandas practice
Linear models and Stochastic gradient descent	1. Loss functions examples 2. Linear classifier 3. Stochastic gradient descent 4. Regularization	📺 Video | 📝 Gradient Descent
Logical Rules and Decision Trees	1. Logical rules 2. Decision trees 3. ID3 algorithm 4. Mesurement of uncertainty	📺 Video | 📝 LR and DT in Cogniterra
Ensembles, gradient boosting and random forest	1. Simple and weighted voting, mixture of experts 2. Boosting, bagging, RSM 3. XGBoost, CatBoost, LightGBM 4. Random forest	📺 Video | 📝 Kaggle Competition
Intro to neural networks and backpropagation	1. Rise of neural networks 2. Expressive power of neural network 3. Backpropagation algorithm	📺 Video | 📝 Simple Neural Network
Recurrent neural networks basics	1. Disadvantages of Feed-Forward Neural Networks 2. Architectures of Recurrent Networks 3. Vanilla RNN, LSTM, GRU	📺 Video | 📝 Contest 1 in Cogniterra
PyTorch Tutorial	1. Working with tensors 2. Autograd and Neural Network example 3. Demo model for word classification	📺 Video | 📝 PyTorch Introduction
Language modeling: bigrams and multi layers perceptron	1. Makemore on the bigrams level 2. Adding prior for more robust predictions 3. Multi Layer Perceptron	📺 Video | 📝 Intro to LM in Cogniterra
Attention and transformers	1. Disadvantages of RNNs 2. Attention mechanism 3. Transformers, BERT	📺 Video | 📝 Backpropagation and MLP
Building GPT-2 from scratch	1. "Attention is all you need" 2. Math trick in self-attention 3. Layer normalization and dropout	📺 Video | 📝 Transformers
Second Part
Convolutional Neural Networks	1. Brief history of computer vision 2. The progress of convolutional neural nets 3. Details of AlexNet model	📺 Video | 📝 CNN Kaggle Competition
Detection and Segmentation in Computer Vision	1. Detection and Segmentation tasks 2. IoU, Average Precision 3. Fast R-CNN, UNet	📺 Video
Semi- and unsupervised learning, clustering	1. K-Means Clustering 2. Principal Component Analysis (PCA) 3. t-SNE, MAP, Other Dimensionality Reduction Methods 4. DBSCAN Clustering, Hierarchical Clustering	📺 Video | 📝Semi- and unsupervised learning, clustering in Cogniterra
CLIP. Fine-tuning and lin-prob	1. Pre-training and fine-tuning 2. CLIP: Contrastive Language-Image Pre-training 3. Zero-shot CLIP is a robust model	📺 Video
Generative vs Discriminative models, VAE, GANs	1. Difference between Generative and Discriminative models 2. What is GAN and how to train it? 3. Variational Autoencoder architecture, reparameterization trick	📺 Video
Reinforcement Learning Intro	1. Reward modeling and Optimal Control Assumptions 2. Environments and Observations 3. Markov Decision Process	📺 Video
From AlphaGo to AlphaZero	1. TD Gammon 2. Why is Go really hard for computer? 3. Inside AlphaGo: SL and RL policies, MCTS 4. Inside AlphaZero	📺 Video | 📝 AlphaZero from Scratch
Stable Diffusion	1. Noising and denoising 2. Pipeline of Stable Diffusion 3. Adding Text Encoder and CLIP	📺 Video
NLP Crash Intro	1. Word2vec and GloVe 2. Transformers, BERT and NLP tasks 3. Fine-tuning and PEFT	📺 Video
Agents with LangChain	1. How LLMs use tools? 2. Chatbot's memory 3. Vector Stores for Embeddings	📺 Video | 📝Build Your Own AI Agent in Cogniterra