pages/100_Graduate_Machine_Learning.py

import streamlit as st

# Set up the main title
st.title("PhD-Level Machine Learning Course")

# Overview of the Course
st.header("Course Overview")
st.write("""
This PhD-level course in Machine Learning is designed to cover both foundational and cutting-edge topics, 
providing a comprehensive understanding of machine learning theory, advanced optimization techniques, probabilistic models, and more. 
The course involves hands-on projects and research problems to solidify your knowledge in this rapidly evolving field.
""")

# Course Objectives
st.header("Course Objectives")
st.markdown("""
- **Understanding Deep Learning Theory**: Dive into the foundational theories behind deep learning models.
- **Exploration of Probabilistic Graphical Models**: Study the application of PGM in ML.
- **Advanced Optimization Techniques**: Explore methods like stochastic gradient descent, variational inference, and more.
- **Cutting-edge Research Areas**: Learn about the latest research in generative models, reinforcement learning, and unsupervised learning.
- **Hands-on Research and Projects**: Implement state-of-the-art models in practical settings.
""")

# Syllabus and Modules
st.header("Course Syllabus")

# Module 1: Foundations and Theoretical Aspects
st.subheader("Module 1: Foundations and Theoretical Aspects")
st.write("""
- **Linear Models**: Linear Regression, Ridge and Lasso Regression, Regularization Techniques.
- **Convex Optimization**: Convex sets, Gradient Descent, Duality.
- **Kernel Methods**: Support Vector Machines, Kernel tricks, Gaussian and Polynomial Kernels.
- **Readings**: "Elements of Statistical Learning" by Hastie, Tibshirani, Friedman.
""")
st.write("### Problems for Module 1:")
st.write("""
1. Implement simple linear regression from scratch using only NumPy. Compare your results with scikit-learn.
2. Solve for the weights of a ridge regression model by manually deriving the closed-form solution.
3. Implement Lasso regression using coordinate descent. Evaluate its performance on a synthetic dataset.
4. Prove that the cost function in linear regression is convex.
5. Solve the constrained optimization problem: minimize f(x) = x^2 subject to x ≥ 1 using gradient descent.
""")

# Module 2: Probabilistic Models in Machine Learning
st.subheader("Module 2: Probabilistic Models in Machine Learning")
st.write("""
- **Bayesian Networks**: Structure learning, Conditional independence, Markov Properties.
- **Hidden Markov Models (HMMs)**: Forward-backward algorithm, Viterbi algorithm, Baum-Welch for parameter estimation.
- **Gaussian Mixture Models (GMMs)**: Expectation-Maximization (EM), Clustering.
- **Readings**: "Pattern Recognition and Machine Learning" by Bishop.
""")
st.write("### Problems for Module 2:")
st.write("""
1. Build a Bayesian network for a medical diagnosis problem. Perform exact inference on this network.
2. Derive the update rules for Bayesian networks and implement them to calculate posterior probabilities.
3. Implement the forward-backward algorithm for HMM and apply it to a sequence prediction problem.
4. Use Gaussian Mixture Models for image segmentation.
5. Implement variable elimination for a small Bayesian network and compare it with junction tree inference.
""")

# Module 3: Advanced Deep Learning
st.subheader("Module 3: Advanced Deep Learning")
st.write("""
- **Generative Adversarial Networks (GANs)**: GAN formulation, Loss functions, Generator vs. Discriminator dynamics.
- **Variational Autoencoders (VAEs)**: Latent variable models, KL Divergence, Reparameterization trick.
- **Deep Reinforcement Learning**: Policy Gradients, Q-learning, Actor-Critic methods.
- **Readings**: "Deep Learning" by Goodfellow, Bengio, Courville.
""")
st.write("### Problems for Module 3:")
st.write("""
1. Implement a basic GAN from scratch using PyTorch and train it on the MNIST dataset.
2. Build a CycleGAN to perform image translation (e.g., transforming horses into zebras) using a public dataset.
3. Implement a Variational Autoencoder (VAE) for dimensionality reduction on the MNIST dataset.
4. Train a Proximal Policy Optimization (PPO) agent for continuous control in the BipedalWalker environment.
5. Derive the loss functions for both the generator and discriminator in GANs and explain their interaction during training.
""")

# Module 4: Reinforcement Learning
st.subheader("Module 4: Reinforcement Learning")
st.write("""
- **Value-Based Methods**: Q-learning, SARSA, Bellman equations, MDPs.
- **Policy-Based Methods**: Policy gradient methods, REINFORCE algorithm, Actor-Critic models.
- **Readings**: "Reinforcement Learning: An Introduction" by Sutton and Barto.
""")
st.write("### Problems for Module 4:")
st.write("""
1. Implement Q-learning from scratch to solve a grid-world environment.
2. Apply SARSA to a stochastic grid-world environment and compare its performance to Q-learning.
3. Implement the REINFORCE algorithm for a simple policy gradient task and analyze the variance in the gradient estimates.
4. Train an actor-critic agent using A2C in a continuous state space environment.
5. Train an agent using PPO in the Humanoid-v2 environment from OpenAI Gym.
""")

# Module 5: Transfer Learning and Meta-Learning
st.subheader("Module 5: Transfer Learning and Meta-Learning")
st.write("""
- **Transfer Learning Concepts**: Fine-tuning, Feature extraction, Adapting pre-trained models.
- **Meta-Learning (Few-Shot Learning)**: Learning-to-learn, MAML, Prototypical Networks.
- **Readings**: Recent advances in transfer learning (BERT, GPT, etc.).
""")
st.write("### Problems for Module 5:")
st.write("""
1. Fine-tune BERT for a text classification task on a custom dataset using Hugging Face.
2. Implement feature extraction from a pre-trained model (e.g., VGG16) and apply it to a new dataset for object detection.
3. Train a Prototypical Network for few-shot learning and test it on a small dataset of handwritten characters.
4. Explore how few-shot learning techniques can be applied to reinforcement learning tasks.
5. Train a transfer learning model on medical images (e.g., X-rays) using DenseNet and analyze the results.
""")

# Module 6: Unsupervised and Self-Supervised Learning
st.subheader("Module 6: Unsupervised and Self-Supervised Learning")
st.write("""
- **Clustering and Dimensionality Reduction**: K-means, PCA, t-SNE, UMAP.
- **Self-Supervised Learning**: SimCLR, BYOL, Denoising Autoencoders.
""")
st.write("### Problems for Module 6:")
st.write("""
1. Implement K-means clustering and apply it to a dataset of customer behavior data to segment users.
2. Use t-SNE to visualize high-dimensional data (e.g., word embeddings) and explore the resulting clusters.
3. Train a self-supervised model on an image dataset and visualize learned features.
4. Implement contrastive learning (SimCLR) to learn visual representations without labels.
5. Apply PCA for dimensionality reduction on a real-world dataset (e.g., image data).
""")

# Module 7: Advanced Optimization Techniques
st.subheader("Module 7: Advanced Optimization Techniques")
st.write("""
- **Stochastic Optimization**: SGD, Adam, RMSProp, Learning Rate Schedules.
- **Variational Inference**: ELBO maximization, Stochastic Variational Inference, Monte Carlo methods.
""")
st.write("### Problems for Module 7:")
st.write("""
1. Implement stochastic gradient descent (SGD) with learning rate schedules and compare different optimization techniques (Adam, RMSProp) on the MNIST dataset.
2. Derive the update rules for the Adam optimizer and implement it from scratch.
3. Train a deep learning model with cyclical learning rates and analyze the training dynamics.
4. Implement variational inference to fit a Bayesian neural network on a small dataset.
5. Explore the impact of weight decay and momentum in training deep networks and visualize their effect on the loss surface.
""")

# Module 8: Special Topics in Machine Learning
st.subheader("Module 8: Special Topics in Machine Learning")
st.write("""
- **Interpretability**: SHAP, LIME, Counterfactual Explanations, Fairness in ML.
- **Adversarial Learning**: FGSM, PGD, Robust models.
- **Causal Inference**: Structural Equation Modeling (SEM), Causal graphs, Counterfactuals.
- **Readings**: "The Book of Why" by Judea Pearl.
""")
st.write("### Problems for Module 8:")
st.write("""
1. Implement an adversarial attack on a deep learning model and build defenses.
2. Use SHAP and LIME to interpret the results of a complex machine learning model.
3. Develop a causal inference model for decision-making in healthcare or economics.
4. Implement counterfactual explanations to improve model interpretability.
5. Explore fairness in machine learning using real-world datasets.
""")

# Assessments Section (continued)
st.write("""
- **Midterm Project**: Design and implement a machine learning model that incorporates advanced theoretical methods and optimization techniques.
- **Final Research Paper**: Write and present a research paper on a selected topic in advanced machine learning, proposing a novel method, or experimenting with cutting-edge models.
""")

# Additional Resources Section
st.header("Additional Resources")
st.write("""
- **Books**:
  - "Elements of Statistical Learning" by Hastie, Tibshirani, Friedman
  - "Pattern Recognition and Machine Learning" by Christopher M. Bishop
  - "Deep Learning" by Ian Goodfellow, Yoshua Bengio, Aaron Courville
  - "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
  - "The Book of Why" by Judea Pearl (for causal inference)
- **Libraries & Tools**:
  - **PyTorch**: A deep learning framework used for various implementations (GANs, VAEs, etc.).
  - **TensorFlow**: Another widely-used deep learning library.
  - **Hugging Face**: Pre-trained models for transfer learning and NLP tasks.
  - **OpenAI Gym**: A toolkit for developing and comparing reinforcement learning algorithms.
  - **Pyro**: A probabilistic programming library built on PyTorch for Bayesian networks and probabilistic models.
- **Research Journals & Conferences**:
  - NeurIPS (Neural Information Processing Systems)
  - ICML (International Conference on Machine Learning)
  - ICLR (International Conference on Learning Representations)
""")

# Footer Section
st.write("Good luck with your studies and projects in advanced machine learning! Stay curious and keep exploring.")

# End of Streamlit App