An introductory exploration of core machine learning concepts, algorithms, and practical applications using Python. This course focuses on foundational topics in supervised and unsupervised learning, emphasizing intuitive understanding and practical implementation rather than exhaustive mathematical depth. Students will gain hands-on experience utilizing essential Python data science libraries including NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn and PyTorch.
Letter grades will be assigned based on your numeric final average:
| A >= 93.0 | A- >= 90.0 | |
| B+ >= 87.0 | B >= 83.0 | B- >= 80.0 |
| C+ >= 77.0 | C >= 73.0 | C- >= 70.0 |
| D+ >= 67.0 | D >= 63.0 | D- >= 60.0 |
| F < 60.0 |
Students will be given one warning if they are late to class. After the warning, any subsequent lateness will be considered an absence and the penalties and policies below will be enforced.
Students can be excused for missing class in cases of bereavement, observance of a religious holiday, serious work obligations, health/medical, or varsity intercollegiate athletic contest. Siena University is committed to diversity and an essential element of this commitment is the recognition of diverse religious practices. If students provide advance notice that they will miss class due to religious practices, the absence will be excused and student can make up work without penalty.
The instructor makes the final decision to excuse or not to excuse an absence. If you are concerned that an absence will not be excused, you should contact the instructor as soon as possible. The following guidelines will be used to make decisions.
Students can have two unexcused absences without any penalty. Subsequent unexcused absences will be penalized as follows:
| # of Absences | Penalty |
|---|---|
| 3 | 1% |
| 4 | 3% |
| 5 | 7% |
| 6 | 13% |
| 7 | 21% |
| 8 | 30% |
| 9 | 40% |
In compliance with the Americans with Disabilities Act and with Section 504 of the Rehabilitation Act, Siena College is committed to ensuring educational access and accommodations for all its registered students. I request that any student with a documented disability needing academic adjustments or accommodations speak with me during the first two weeks of class. All discussions will remain confidential. Students with disabilities should also contact Ms. Lindsay Green, Director of the Office of Accessibility. Ms. Green’s office is located in Foy Hall Room 109. Her phone number is: (518) 783-4239. Complaints about services provided or not provided may be brought to the attention of Public Safety at (518) 783-2376 or Siena's Title IX Coordinator and Equal Opportunity Specialist located in Room 235 of the Sarazen Student Union.
In our Franciscan community, diversity is an invitation to celebrate the uniqueness of each individual, as well as the cultural differences that enrich us all. In this course, I will do my best to ensure that students from all backgrounds and perspectives will be served equitably. The diversity that students bring to this class will be viewed as a resource, strength and benefit. It is my intent to present materials and activities that are respectful and inclusive of the many identities of students in terms of gender, sexual orientation, disability, age, socioeconomic status, ethnicity, race, culture, perspective, and other background characteristics. Your suggestions about how to improve the value of diversity and inclusiveness in this course are encouraged and appreciated.
Students are asked to indicate their preferred pronouns by updating their profile in Canvas. The use of preferred pronouns is important to show respect for students’ gender identities, and to recognize and validate students’ right to choose their own names and forms of address. Please use preferred pronouns that other students display in oral and written comments to them during class discussions.
In any case of possible bias or sexual misconduct, either in the classroom or anywhere on campus, you are encouraged to file a report with Public Safety (available 24/7) or in person with Samantha Faiella, Siena’s Deputy Title IX/Title VII Coordinator, SSU 310, 518-783-2328, sfaiella@siena.edu. There is also an online reporting option for bias-related incidents (Search: Siena College Bias Report). Any Siena community member, who experiences or observes an incident of bias or misconduct, including faculty, staff and students, can file a report through this system. More information and resources around procedures dealing with sexual misconduct and discrimination and harassment are available online.
During an exam period, students cannot share information, look at each other's tests, or use unauthorized materials. Using an Internet search or an AI tool for answering a question is explicitly prohibited. The instructor will inform students in advance if the textbook or other materials can be used for a particular exams. Students caught cheating on an exam, will receive a zero on the exam and will be penalized a full letter-grade in the course.
It is very easy to copy Python code from classmates, AI tools, or other sources and claim it as your own. This is academically dishonest and considered plagiarism. Students who present other authors' code, documents or answers as their own will receive a grade of zero on the entire assignments. But, in many circumstances, it is considered professionally acceptable to use open source code and data as long as such usage is documented by giving the original author credit in any newly created work. However, you cannot use classmates as sources. Documenting sources should be done by using citations and/or comments in source files. Note that it is very important to cite your sources before you submit your work.
A student caught cheating a 2nd time will automatically fail the course and a letter describing the student's violation will be sent to Siena's Provost. Note that Siena has expelled students for repeat academic integrity violations in multiple courses.
The goal is for students to learn by doing, not by copying AI generate solutions. For this course specifically, AI tools may be used for low-level programming help and high-level conceptual questions, but not for directly solving problems. Ultimately, you should be able to explain all the work you submit and be able to write code by hand for in-person exams. Your handwritten code can receive full credit even if it has minor mistakes. The key is demonstrating enough knowledge that you could be successful without AI.
AI agents should function as teaching aids that help students learn through explanation, guidance, and feedback — not by completing assignments. Students are expected to write Python code without AI assistance on exams. Use AI to help you understand code so that you can write it yourself.
Student: "My tokenizer is slow. How can I make it faster?"
Agent: "Do you know which part of your tokenizer is slow? Let's start by profiling it. What profiling data do you already have?"
Student: "Fix my tokenizer and make it faster."
Agent: "Here's the full python code: ..."
| Week | Date | Session | Topic & Programming Activity |
|---|---|---|---|
| Week 1: Introduction to Machine Learning | Wed Sep 9 | Lecture 1 | Course Intro & ML Paradigms: Introduction to Supervised vs. Unsupervised Learning and the Python Data Science Ecosystem. |
| Fri Sep 11 | Lab 1 |
Environment Setup & Scientific Computing
Lab Assignment 1:
Set up the Python development environment and practice vectorization, array manipulation, and matrix operations using NumPy.
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| Week 2: Data Exploration & Preprocessing | Mon Sep 14 | Lecture 2 | Data Representation: Tabular data structures and Exploratory Data Analysis (EDA) using Pandas. |
| Wed Sep 16 | Lecture 3 | Data Preprocessing: Handling missing data, imputation strategies, and categorical encoding (One-Hot / Label). | |
| Fri Sep 18 | Lab 2 |
Data Cleaning and Exploratory Analysis
Lab Assignment 2:
Load raw tabular datasets, perform missing value imputation and encoding, and generate descriptive visualizations using Pandas and Matplotlib.
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| Week 3: Distance Metrics & Nearest Neighbors | Mon Sep 21 | Lecture 4 | Feature Spaces & Distance: Geometric feature representation, Euclidean vs. Manhattan distance, and scaling. |
| Wed Sep 23 | Lecture 5 | k-Nearest Neighbors: The k-NN classification algorithm, voting rules, and choice of k. | |
| Fri Sep 25 | Lab 3 |
Implementing k-Nearest Neighbors Classification
Lab Assignment 3:
Build and evaluate a k-NN classifier on structured datasets using Scikit-Learn, examining the impact of feature scaling and parameter tuning.
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| Week 4: Model Evaluation & Linear Regression | Mon Sep 28 | Lecture 6 | Model Evaluation: Train/Test splits, Confusion Matrices, Precision, Recall, and F_1-score. |
| Wed Sep 30 | Lecture 7 | Simple Linear Regression: Modeling continuous targets, residual errors, and Mean Squared Error (MSE). | |
| Fri Oct 2 | Lab 4 |
Model Evaluation & Simple Linear Regression
Lab Assignment 4:
Implement performance evaluation metrics from scratch and fit simple linear regression models to continuous data in Scikit-Learn.
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| Week 5: Gradient Descent & Multiple Linear Regression | Mon Oct 5 | Lecture 8 | Optimization Intuition: Loss surfaces, cost functions, and Gradient Descent fundamentals. |
| Wed Oct 7 | Lecture 9 | Multiple Linear Regression: Multi-feature regression and feature standardization vs. Min-Max scaling. | |
| Fri Oct 9 | Lab 5 |
Multiple Linear Regression & Gradient Optimization
Lab Assignment 5:
Construct multiple linear regression models on multi-dimensional datasets, apply feature standardization, and interpret learned coefficients.
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| Week 6: Logistic Regression & Classification | Mon Oct 12 | Lecture 10 | Logistic Regression: Sigmoid activation function and probabilistic binary classification. |
| Wed Oct 14 | Lecture 11 | Decision Boundaries: Linear classification boundaries and the Log-Loss cost function. | |
| Fri Oct 16 | No Class | NO CLASS SCHEDULED (No Friday Lab session). | |
| Week 7: Regularization & Overfitting | Mon Oct 19 | No Class | NO CLASS SCHEDULED |
| Wed Oct 21 | Lecture 12 | Regularization: Overfitting vs. underfitting trade-offs, L1 (Lasso), and L2 (Ridge) penalties. | |
| Fri Oct 23 | Lab 6 |
Logistic Regression & Regularization
Lab Assignment 6:
Train binary classification models using Logistic Regression and analyze how L1 and L2 regularization impact model bias and variance.
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| Week 8: Decision Trees & Midterm | Mon Oct 26 | Lecture 13 | Decision Trees: Recursive splitting rules, Information Gain, Entropy, and Gini Impurity. |
| Wed Oct 28 | Lecture 14 | Tree Pruning & Review: Controlling tree depth, preventing overfitting, and Midterm Review. | |
| Fri Oct 30 | Midterm |
MIDTERM EXAM
Administered during the Friday Lab period (8:00 AM – 10:00 AM) in RB 222. Open Books. No AI. No Google. No lab assignment.
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| Week 9: Ensemble Methods (Part 1) | Mon Nov 2 | Lecture 15 | Ensemble Methods: Introduction to Bagging (Bootstrap Aggregating) and variance reduction. |
| Wed Nov 4 | No Class | NO CLASS SCHEDULED | |
| Fri Nov 6 | No Class | NO CLASS SCHEDULED (No Friday Lab session). | |
| Week 10: Decision Trees & Ensemble Methods (Part 2) | Mon Nov 9 | Lecture 16 | Random Forests: Random feature selection, decision tree ensembles, and feature importance metrics. |
| Wed Nov 11 | Lecture 17 | Hyperparameter Tuning: k-fold Cross-Validation and Grid Search strategies. | |
| Fri Nov 13 | Lab 7 |
Decision Trees and Random Forest Ensembles
Lab Assignment 7:
Train Decision Tree and Random Forest models on tabular datasets, tune tree depth, and extract feature importance scores.
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| Week 11: Support Vector Machines & Model Tuning | Mon Nov 16 | Lecture 18 | Support Vector Machines (SVM): Hyperplane geometry, margin maximization, and support vectors. |
| Wed Nov 18 | Lecture 19 | Kernel Trick: Non-linear classification using RBF and Polynomial kernels in SVMs. | |
| Fri Nov 20 | Lab 8 |
Support Vector Machines & Hyperparameter Tuning
Lab Assignment 8:
Implement Linear and RBF-kernel Support Vector Classifiers, applying Scikit-Learn's GridSearchCV for hyperparameter optimization.
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| Week 12: Unsupervised Learning & Clustering | Mon Nov 23 | Lecture 20 | Unsupervised Learning: Introduction to Clustering and the k-Means Algorithm. |
| Wed Nov 25 | No Class | NO CLASS SCHEDULED (Thanksgiving Break) | |
| Fri Nov 27 | No Class | NO CLASS SCHEDULED (Thanksgiving Break) | |
| Week 13: Clustering Evaluation & Dimensionality Reduction | Mon Nov 30 | Lecture 21 | Cluster Validation: The Elbow Method, Inertia, and Silhouette Coefficient analysis. |
| Wed Dec 2 | Lecture 22 | Dimensionality Reduction: Principal Component Analysis (PCA) intuition and variance retention. | |
| Fri Dec 4 | Lab 9 |
k-Means Clustering & PCA Dimensionality Reduction
Lab Assignment 9:
Cluster unlabelled tabular data with k-Means, evaluate clusters using Silhouette scores, and project high-dimensional data via PCA for 2D visualization.
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| Week 14: Neural Network Fundamentals | Mon Dec 7 | Lecture 23 | Neural Networks Intro: Biological motivation, the Rosenblatt Perceptron, and linear decision boundaries. |
| Wed Dec 9 | Lecture 24 | Multi-Layer Perceptrons (MLP): Hidden layers, feedforward architecture, and activation functions (ReLU, Softmax). | |
| Fri Dec 11 | Lab 10 |
Multi-Layer Perceptron (MLP) Neural Networks
Lab Assignment 10:
Build, train, and evaluate a Multi-Layer Perceptron classifier using Scikit-Learn's MLPClassifier on non-linearly separable datasets.
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| Week 15: Ethics in ML & Course Synthesis | Mon Dec 14 | Lecture 25 | ML Ethics & Synthesis: Algorithmic bias, fairness, data privacy, and comprehensive course review. |
| Dec 16 – Dec 19 | Final Exam |
FINAL EXAM PERIOD
Final Exam scheduled during official college exam week (Dec 16–19). Students must be prepared for the possibility of the final exam being scheduled as late as Saturday, December 19.
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