DSA Foundation & ML Alignment
Weeks 1: Core DSA Basics
- 30 min - Core DSA practice: Arrays, Strings, Lists, Stacks, Queues, Trees, Graphs, sorting, Big-O
- Python / MATLAB practice with small examples
- 30 min – ML concept from Bishop or Alpaydin: Linear Regression with features implementation (Python/Numpy/Scikit-learn)
- 30 min – Reflect & notes: connect DSA ↔ ML concept
- Binary trees, traversal, recursion
- Decision Trees (ID3/CART)
- DSA tie-in: recursion & search
- Project: small decision tree classifier
Use FreeCodeCamp DSA playlist, Grokking Algorithms, NeetCode, LeetCode Easy problems.
Implement mini-projects linking DSA to ML: KNN, Decision Tree, Naive Bayes.
Weeks 3: ML Algorithm Practice
- Linear Models: arrays, vector operations
- Decision Trees: recursion, binary trees
- KNN & Nearest Neighbors: sorting, distance calculations, project: toy dataset classifier
- Neural Networks: matrix multiplication, graph traversal
- Genetic Algorithms: arrays, heaps
- Sorting & searching algorithms
- DSA tie-in: k-d trees, distance measures
Read corresponding chapters in Machine Learning – An Algorithmic Perspective and implement each from scratch.
Test models on synthetic datasets to visualize results. Track progress daily, write reflections, visualize results, and integrate DSA & ML for best learning.
Round 1: 3-Week Daily Checklist
Week 1: Probabilistic Models & Classification
Goal: Bayes theorem, Naïve Bayes, Gaussian distributions, conditional probabilities.
Day 1: Bayes Theorem / Class Probability
Day 2: Conditional & Joint Probability
Day 3: PDFs & PMFs - generate Gaussian/Uniform distributions
Day 4: Expectation & Variance - calculate mean, variance, covariance
Day 5: Sampling & Histograms - sample and compare to theoretical PDFs
Day 6: Vectors - dot product, norm, visualize in 2D
Day 7: Matrices - multiplication, inverse, determinant, transpose
Week 2: Regression & Generalization
Goal: Linear regression, polynomial regression, regularization, cross-validation, overfitting.
Day 8: Patterns & Features - create and visualize dataset
Day 9: k-Nearest Neighbor - implement simple 2D k-NN
Day 10: Naive Bayes Classifier - implement for two features
Day 11: Linear Decision Boundary - plot boundaries
Day 12: Non-linear Decision Boundary - polynomial/quadratic boundaries
Day 13: Feature Scaling & Normalization - scale and visualize Day
Day 14: Covariance & Correlation - compute and visualize
Week 3: Clustering & Generative Models
Goal: Gaussian Mixture Models, EM algorithm, k-Means, cluster evaluation.
Day 15: k-Means Clustering, cluster and plot centers
Day 16: Gaussian Mixture / EM Algorithm fit and visualize
Day 17: Cluster Validity silhouette or Davies-Bouldin score
Day 18: PCA project data to principal components
Day 19: LDA Fishers Linear Discriminant for 2-class data
Day 20: Logistic Regression binary classification, decision boundary
Day 21: Integration & 3D Visualization combine clustering, PCA, classifiers
Round 2: Reinforcement Learning: Concept Questioning
Week 1: RL Foundations & MDPs
Goal: Understand the building blocks: states, actions, rewards, and transitions.
Day 1: What is a Markov Decision Process (MDP)? What are states, actions, rewards, and transitions? Give a real-world example.
Day 2: What is a policy? How does it differ from a value function? Why do we need both?
Day 3: What is the difference between model-based and model-free RL? When would you use each?
Day 4: What is Monte Carlo prediction? How does it estimate the value of a state? What are its limitations?
Day 5: What is Temporal Difference (TD) learning? How does it differ from Monte Carlo? Why is it useful for online learning?
Day 6: How does recursion or search in DSA relate to exploring states in RL?
Day 7: Reflect: How do DSA concepts (arrays, trees, recursion) connect to RL state exploration?
Week 2: Tabular RL & Policy Learning
Goal: Understand Q-Learning, SARSA, and basic policies.
Day 8: What is Q-Learning? What does the Q-table represent? How is the Q-value updated?
Day 9: What is SARSA? How does it differ from Q-Learning? When would SARSA perform better?
Day 10: What is a policy gradient? How does it differ from value-based methods?
Day 11: What is the ε-greedy policy? Why do we need exploration in RL?
Day 12: What is reward shaping? How can modifying rewards affect learning?
Day 13: How does changing the environment (goal location, obstacles) affect the learning process?
Day 14: Reflect: Compare Q-Learning, SARSA, and policy gradient. When would you pick each approach?
Week 3: Advanced RL & Function Approximation
Goal: Connect RL to ML concepts like function approximation, dimensionality reduction, and clustering.
Day 15: What is function approximation in RL? Why can’t we always use tabular methods?
Day 16: How can clustering (e.g., k-Means) help reduce the state space in RL?
Day 17: How does PCA or dimensionality reduction assist in RL when dealing with high-dimensional states?
Day 18: How can neural networks approximate value functions or policies? What are the risks?
Day 19: How can you combine tabular methods with function approximation in a single RL agent?
Day 20: How do you evaluate an RL agent? What metrics indicate learning success?
Day 21: Reflect: How do DSA, ML, and RL concepts integrate? What patterns connect them?
Tips for Using This Round
- Answer each question in writing before looking at code.
- Draw diagrams: MDPs, Q-tables, state transitions, policies.
- Relate answers to your Round 1 ML + DSA mini-projects.
- Use Python/Gym to verify your reasoning after conceptual answers.