Create 100_Graduate_Machine_Learning.py

pages/100_Graduate_Machine_Learning.py

@@ -0,0 +1,161 @@
+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
+st.header("Assessments")
+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