Statistical Machine Learning

1. Overview & Logistics

👨‍🏫 Instructor: Mejbah Ahammad
🗓️ Semester: Spring Semester
⏰ Class Time: 8:00 PM – 10:00 PM
📅 Class Days: Saturday and Wednesday
💻 Class Mode: Remote (Zoom)
💰 Course Fee: ৳4000
☎️ Contact Number: +8801874603631
⌚ Lessons & Time: 20 Lessons, 40 ঘন্টা 20 মিনিট total
📧 Email: hello@softwareintelligence.ai
🌐 Website: http://softwareintelligence.ai/

2. Course Description

Statistical Machine Learning combines:

🎓 Statistical Foundations: Probability, distributions, estimation, hypothesis testing
📈 Machine Learning Techniques: Regression, classification, clustering, ensemble methods
💻 Practical Coding: Python (NumPy, pandas, scikit-learn) for model building and evaluation
🔎 Advanced Topics: Bayesian inference, neural networks, SVMs, interpretability, ethics
🗣️ Communication: Presentation, reporting, real-world problem-solving

By course end, participants will build robust ML pipelines using statistical rigor and cutting-edge techniques, culminating in a capstone-style final project or demonstration.

3. Learning Outcomes

🎓 Foundational Skills (Beginner)
- 👉 Recognize basic probability concepts, distributions, and inference methods.
- 👉 Perform initial data cleaning, wrangling, and EDA in Python.
📈 Intermediate Skills
- 👉 Develop supervised ML models (linear/logistic regression, ensembles).
- 👉 Understand regularization, model tuning, and cross-validation strategies.
💡 Advanced Skills
- 👉 Implement Bayesian methods, SVMs, neural networks, or advanced ensemble strategies.
- 👉 Critically analyze model assumptions, interpret results, and address ethical concerns.
🗣️ Professional Communication
- 👉 Present findings to diverse audiences with clarity (visuals, reports).
- 👉 Collaborate effectively, incorporating peer/instructor feedback.

4. Prerequisites

🎓 Mathematics & Statistics:
- Basic probability (Normal, Binomial), inferential stats (t-tests, p-values).
- Some exposure to linear algebra (matrix operations) and calculus (derivatives).
💻 Programming:
- Familiarity with Python (lists, loops, functions).
- Basic usage of pandas, NumPy, matplotlib, or similar.
💼 Logistics & Tools:
- Stable internet for Zoom.
- Python environment (Anaconda recommended).
- Willingness to install additional Python packages (e.g., scikit-learn).

5. Course Materials

A. Required Texts/Readings

📗 An Introduction to Statistical Learning (ISL) by James, Witten, Hastie, Tibshirani (Springer).
📙 The Elements of Statistical Learning (ESL) by Hastie, Tibshirani, Friedman (Springer).

B. Recommended

📒 Pattern Recognition and Machine Learning by Christopher Bishop (Springer).
📕 Bayesian Data Analysis by Gelman et al. (CRC Press).
Official Python & scikit-learn documentation.

C. Software

💻 Python 3.x (Anaconda)
📓 Jupyter Notebook / IDE (VSCode, PyCharm)
🖥️ Zoom for remote sessions

6. Schedule & Lessons (20 Classes, 40 Hours 20 Minutes)

Each lesson is designed for approximately 2 hours (some might slightly exceed to total 40:20). Classes blend theory and hands-on demos or exercises.

Lesson	Topic	Level	Key Focus
1	📍 Course Introduction & Probability Review	Beginner	Syllabus, environment setup, distributions (Bernoulli, Normal), random variables
2	📍 Parameter Estimation (MLE & MAP)	Beginner → Intermediate	Likelihood functions, Bayesian vs. frequentist approaches, small coding demos
3	📍 Exploratory Data Analysis & Hypothesis Testing	Beginner → Intermediate	Data wrangling, missing values, EDA, t-tests, p-values, confidence intervals
4	📍 Linear Regression & Regularization	Intermediate	OLS, Ridge, Lasso, cross-validation, bias-variance trade-off
5	📍 Logistic Regression & Classification Metrics	Intermediate	Confusion matrix, precision/recall, ROC-AUC, cross-entropy
6	📍 Feature Engineering & Model Diagnostics	Intermediate	Categorical encoding, polynomial features, residual analysis, error estimation
7	📍 Bayesian Methods & Conjugate Priors	Intermediate → Advanced	Posterior updates, Beta-Bernoulli, Normal-Normal, MCMC basics
8	📍 Decision Trees & Ensemble Methods (Bagging, RF)	Intermediate → Advanced	CART, random forests, OOB errors, feature importance, bagging strategies
9	📍 Boosting & Advanced Ensemble (AdaBoost, XGBoost)	Intermediate → Advanced	Gradient boosting, sequential error correction, hyperparameter tuning
10	📍 Support Vector Machines (Foundations)	Intermediate → Advanced	Margin maximization, kernel trick, soft/hard margins
11	📍 SVM in Practice & Tuning	Advanced	RBF, polynomial kernels, grid/random search, practical pitfalls
12	📍 Dimensionality Reduction (PCA, LDA)	Intermediate → Advanced	Covariance, eigen-decomposition, linear discriminants, advanced manifold methods (optional)
13	📍 Clustering (K-means, Hierarchical, DBSCAN)	Intermediate → Advanced	Unsupervised basics, cluster validation (silhouette), dendrograms, density-based methods
14	📍 Probabilistic Graphical Models (Bayesian Networks)	Advanced	Conditional independence, factorization, small examples, potential software for inference
15	📍 Hidden Markov Models & Sequential Data	Advanced	Markov chains, forward-backward algorithm, Viterbi decoding, time-series aspects
16	📍 Neural Networks (MLP Intro)	Advanced	Perceptron, activation functions, backprop basics, capacity vs. data requirements

7. Detailed Lesson Descriptions

Lesson 1 (2 Hours)

📌 Topic: Course Introduction & Probability Review

🔎 Focus: Syllabus overview, environment check, distributions (Bernoulli, Normal), sampling basics
👉 Assignment:
- Install libraries (NumPy, pandas, scikit-learn).
- Short quiz on probability concepts.
💼 Professional Insight:
- Probability underpins risk modeling (finance, insurance) and quality control (manufacturing).
- Proper environment setup mirrors DevOps best practices for reproducible data science.

Lesson 2 (2 Hours)

📌 Topic: Parameter Estimation (MLE & MAP)

🔎 Focus: Likelihood functions, frequentist vs. Bayesian viewpoint, small coding demos
👉 Assignment:
- Compare MLE and MAP estimates on a simple dataset (e.g., coin toss).
💼 Professional Insight:
- MLE vs. MAP is critical in marketing analytics (conversion rates) or medical testing (disease prevalence).
- Choosing an appropriate prior (MAP) can incorporate domain knowledge in real-world deployments.

Lesson 3 (2 Hours)

📌 Topic: Exploratory Data Analysis & Hypothesis Testing

🔎 Focus: Data wrangling, missing value handling, outlier detection, t-tests, p-values
👉 Assignment:
- Clean a real dataset, run basic hypothesis tests (A/B style).
💼 Professional Insight:
- EDA is ~80% of real data science tasks. Quick hypothesis tests guide business decisions (new product vs. old).
- Communicating results in non-technical terms fosters stakeholder trust.

Lesson 4 (2 Hours)

📌 Topic: Linear Regression & Regularization (Ridge, Lasso)

🔎 Focus: OLS assumptions, bias-variance, cross-validation, controlling overfitting
👉 Assignment:
- Compare OLS vs. Ridge vs. Lasso on a regression problem (e.g., housing).
💼 Professional Insight:
- Common approach for pricing strategy, sales forecasting, and resource planning.
- Regularization ensures stability in production, saving compute costs by preventing overfitting.

Lesson 5 (2 Hours)

📌 Topic: Logistic Regression & Classification Metrics

🔎 Focus: Confusion matrix, precision/recall, ROC-AUC, threshold tuning
👉 Assignment:
- Classify Titanic-like data; interpret different metrics.
💼 Professional Insight:
- Logistic regression is key in credit scoring, churn prediction, and medical diagnosis.
- Understanding metrics aligns models with business objectives (precision vs. recall trade-offs).

Lesson 6 (2 Hours)

📌 Topic: Feature Engineering & Model Diagnostics

🔎 Focus: Encoding categorical variables, polynomial transformations, residual/error analysis
👉 Assignment:
- Enhance feature set, compare performance gains, analyze errors thoroughly.
💼 Professional Insight:
- In real-world ML, feature engineering often trumps fancy algorithms in terms of improvement.
- Detailed error analysis helps refine future data collection and domain strategies.

Lesson 7 (2 Hours)

📌 Topic: Bayesian Methods & Conjugate Priors

🔎 Focus: Posterior derivation, Beta-Bernoulli, Normal-Normal, intro to MCMC tools
👉 Assignment:
- Perform Bayesian inference on a small dataset; compare to frequentist results.
💼 Professional Insight:
- Bayesian methods handle low-data or high-uncertainty environments (startups, medical research).
- MCMC used in complex risk modeling (insurance, environmental studies).

Lesson 8 (2 Hours)

📌 Topic: Decision Trees & Ensemble Methods (Bagging, RF)

🔎 Focus: CART, random forests, OOB errors, feature importance
👉 Assignment:
- Fit a decision tree & a random forest, compare error rates & interpret features.
💼 Professional Insight:
- Random forests are widely used in finance, healthcare, e-commerce for their interpretability & performance.
- Bagging strategies often reduce variance in high-stakes fields (credit risk, fraud detection).

Lesson 9 (2 Hours)

📌 Topic: Boosting & Advanced Ensemble (AdaBoost, XGBoost)

🔎 Focus: Sequential error correction, gradient boosting, hyperparameter tuning
👉 Assignment:
- Evaluate AdaBoost vs. XGBoost on a classification/regression dataset; tune parameters.
💼 Professional Insight:
- XGBoost dominates Kaggle competitions & corporate environments (marketing, sales forecasting).
- Boosting algorithms can quickly overfit if not carefully tuned—an important skill in production ML.

Lesson 10 (2 Hours)

📌 Topic: Support Vector Machines (Foundations)

🔎 Focus: Margin maximization, kernel trick, soft/hard margins
👉 Assignment:
- Implement SVM on a 2D classification problem, visualize decision boundaries.
💼 Professional Insight:
- SVMs excel in high-dimensional data (text classification, genetics).
- Understanding kernel selection is vital for certain image or speech tasks.

Lesson 11 (2 Hours)

📌 Topic: SVM in Practice & Tuning

🔎 Focus: RBF, polynomial kernels, grid/random search, practical pitfalls
👉 Assignment:
- Use GridSearchCV to tune hyperparameters (C, gamma) on a real dataset.
💼 Professional Insight:
- Proper parameter tuning can drastically shift model accuracy.
- SVM remains a strong baseline in many industrial AI solutions.

Lesson 12 (2 Hours)

📌 Topic: Dimensionality Reduction (PCA, LDA)

🔎 Focus: Covariance, eigen-decomposition, supervised vs. unsupervised dimension reduction
👉 Assignment:
- Apply PCA to a high-dimensional dataset; optionally compare LDA for classification.
💼 Professional Insight:
- Reducing dimensionality helps in visualization and speed for real-time applications (IoT, sensor data).
- LDA commonly used in face recognition, medical classification tasks.

Lesson 13 (2 Hours)

📌 Topic: Clustering (K-means, Hierarchical, DBSCAN)

🔎 Focus: Unsupervised basics, cluster validation, dendrograms, density-based algorithms
👉 Assignment:
- Compare at least two clustering methods, interpret results with silhouette scores.
💼 Professional Insight:
- Clustering widely used in customer segmentation, market research, and anomaly detection.
- DBSCAN or hierarchical clustering can reveal irregular cluster structures in real data.

Lesson 14 (2 Hours)

📌 Topic: Probabilistic Graphical Models (Bayesian Networks)

🔎 Focus: Graph structures, conditional independence, small examples
👉 Assignment:
- Construct or analyze a simple Bayesian network; perform basic inference.
💼 Professional Insight:
- Graphical models appear in medical diagnosis (causal inference), sensor fusion, and complex decision-making.
- Visualizing dependencies helps stakeholders grasp complicated relationships.

Lesson 15 (2 Hours)

📌 Topic: Hidden Markov Models & Sequential Data

🔎 Focus: Markov chains, forward-backward, Viterbi decoding, possible link to time series
👉 Assignment:
- Implement an HMM for a toy sequence (e.g., weather states or text).
💼 Professional Insight:
- HMMs are cornerstones in speech recognition, bioinformatics (DNA sequences), and POS tagging in NLP.
- Sequential modeling is crucial in many real-time or streaming applications.

Lesson 16 (2 Hours)

📌 Topic: Neural Networks (MLP Intro)

🔎 Focus: Perceptron/MLP basics, activation functions, high-level backprop
👉 Assignment:
- Train a small MLP on a classification dataset, discuss overfitting.
💼 Professional Insight:
- Neural nets power advanced computer vision and NLP tasks in top tech companies.
- Balancing model complexity vs. data is critical in production cost management.

Lesson 17 (2 Hours)

📌 Topic: Overfitting & Robust Validation

🔎 Focus: Dropout (in neural nets), early stopping, nested CV, data augmentation
👉 Assignment:
- Show how advanced validation and regularization reduce overfitting in a prior model.
💼 Professional Insight:
- Overfitting leads to financial losses (poor predictions) or misdiagnoses (healthcare).
- Rigorous validation fosters trust and reliability in deployed ML systems.

Lesson 18 (2 Hours)

📌 Topic: Interpretability & Fairness in ML

🔎 Focus: Tools (LIME, SHAP), fairness and bias, ethical frameworks (GDPR)
👉 Assignment:
- Analyze model outputs with SHAP on a selected dataset; discuss potential biases.
💼 Professional Insight:
- Explainable AI is increasingly required in regulated sectors (finance, healthcare).
- Addressing bias fosters equitable and responsible AI solutions.

Lesson 19 (2 Hours)

📌 Topic: Capstone Project Workshop

🔎 Focus: Data selection, model design, refining scope, peer/instructor Q&A
👉 Assignment:
- Prepare a prototype or outline for your final project.
💼 Professional Insight:
- Mimics team stand-ups or project reviews in corporate data science.
- Early feedback loop ensures agile methodology and timely pivots.

Lesson 20 (2 Hours + 20 mins)

📌 Topic: Capstone Presentations & Course Wrap-Up

🔎 Focus: Student/Team presentations, Q&A, advanced resources, next steps in ML
👉 Deliverable:
- Final code/report + demonstration.
- Course feedback or survey.
💼 Professional Insight:
- Polished presentations simulate pitching to executives or clients.
- Reflecting on advanced directions (deep learning frameworks, big data) fosters continual growth.

8. Assessment & Grading

📄 Weekly/Regular Assignments (40%)
- 👉 Coding tasks, problem-solving, reflection papers.
- Reinforces theory with hands-on practice.
📝 Quizzes (10%)
- 👉 Short checks on stats & ML fundamentals (announced or pop).
- Encourages consistent revision.
💼 Capstone Project (40%)
- 👉 End-to-end ML pipeline: data prep → modeling → validation → interpretability → presentation.
- Demonstrates integrated skills from the entire course.
🤝 Participation (10%)
- 👉 Active engagement, Q&A, breakout rooms, peer feedback.
- Collaboration skill is essential in real-world DS teams.

🏷️ Grade Scale

A = 90–100%
B = 80–89%
C = 70–79%
D = 60–69%
F = < 60%

9. Course Policies

🏷️ Attendance & Engagement
- Mandatory Zoom attendance (camera on recommended).
- Inform absences in advance if possible.
📢 Communication
- Check email regularly for announcements.
- Email hello@softwareintelligence.ai for questions or clarifications.
⏲️ Late Submissions
- May incur penalties unless pre-approved.
- Discuss extensions for valid reasons (health, emergencies).
⚠️ Academic Integrity
- No plagiarism or unapproved collaboration.
- Violations follow institutional guidelines.
💻 Technical Setup
- Install/maintain Python environment (Anaconda).
- Ensure stable internet, Zoom readiness.

10. Final Note

Welcome to Statistical Machine Learning! Over 20 Lessons (total 40 hours 20 minutes), expect an interactive deep dive into stats + ML. Keep in mind:

Practice consistently with real datasets.
Engage with peers for feedback and troubleshooting.
Document your processes—transparency is key to professional data science.

We look forward to a dynamic, hands-on semester together!

👨‍🏫 Instructor: Mejbah Ahammad
☎️ Phone: +8801874603631
🌐 Website: http://softwareintelligence.ai/
📧 Email: hello@softwareintelligence.ai