Course Catalog
12 Pilot Courses
Structured course records with learning outcomes, OER license metadata, and transparent development status. Only reviewed, published content is served to students.
Undergraduate Courses
Calculus I
Limits, continuity, differentiation of algebraic and transcendental functions, applications of the derivative, introduction to integration, the Fundamental Theorem of Calculus.
Learning Outcomes
- Apply limits, derivatives, and basic integrals to real-valued functions
- Solve optimization problems using calculus techniques
- Interpret the geometric and physical meaning of derivatives and integrals
Prerequisites
None
OER Source & License
LibreTexts Calculus (CC BY-NC-SA)
Calculus II
Techniques of integration, applications of the integral, sequences and series, Taylor polynomials, parametric equations, and polar coordinates.
Learning Outcomes
- Evaluate definite and improper integrals using advanced techniques
- Analyze sequences and series for convergence
- Apply parametric and polar coordinates
Prerequisites
MATH 101
OER Source & License
LibreTexts Calculus (CC BY-NC-SA)
Linear Algebra
Vector spaces, matrices, linear transformations, systems of equations, determinants, eigenvalues and eigenvectors, diagonalization, and inner product spaces.
Learning Outcomes
- Solve systems of linear equations using matrix methods
- Compute eigenvalues, eigenvectors, and matrix decompositions
- Apply linear transformations and vector spaces to engineering problems
Prerequisites
MATH 101
OER Source & License
MIT OCW 18.06 (CC BY-NC-SA)
Probability and Statistics
Probability theory, random variables, distributions, expectation, statistical inference, hypothesis testing, regression, and applications in engineering and computing.
Learning Outcomes
- Apply probability models to engineering uncertainty
- Select and apply appropriate statistical inference methods
- Conduct hypothesis tests and build confidence intervals
Prerequisites
MATH 101
OER Source & License
LibreTexts Statistics (CC BY)
Programming Fundamentals
Introduction to programming using Python. Variables, expressions, statements, functions, loops, conditionals, lists, dictionaries, file I/O, and introductory object-oriented programming.
Learning Outcomes
- Write correct Python programs using control flow, functions, and data structures
- Decompose problems into algorithmic steps
- Test and debug programs systematically
Prerequisites
None
OER Source & License
Think Python 2e, Allen Downey (CC BY-NC)
Data Structures and Algorithms
Arrays, linked lists, stacks, queues, trees, heaps, hash tables, graphs. Searching, sorting, graph traversal, dynamic programming, and algorithm complexity analysis.
Learning Outcomes
- Implement and analyze fundamental data structures
- Design algorithms and analyze time and space complexity
- Select appropriate data structures for real engineering problems
Prerequisites
CS 101
OER Source & License
MIT OCW 6.006 (CC BY-NC-SA)
Computer Networks
Network architectures, the layered model, application layer protocols, transport layer services, network layer routing, link layer, and an introduction to network security.
Learning Outcomes
- Explain the TCP/IP protocol stack and its layers
- Analyze routing, congestion, and reliable data transfer mechanisms
- Design basic network applications and diagnose common issues
Prerequisites
CS 101
OER Source & License
Computer Networking: A Top-Down Approach (Kurose & Ross) — original authored content with citations
Operating Systems
Processes, threads, CPU scheduling, deadlocks, memory management, virtual memory, file systems, I/O, and security. Programming projects in C.
Learning Outcomes
- Explain process management, scheduling, and synchronization
- Describe memory management, virtual memory, and demand paging
- Analyze file system design and I/O subsystems
Prerequisites
CS 201
OER Source & License
Operating Systems: Three Easy Pieces, Arpaci-Dusseau (CC BY-NC-ND) — review required
Signals and Systems
Continuous- and discrete-time signals and systems, convolution, Fourier series and transforms, Laplace transforms, Z-transforms, sampling, and filter design fundamentals.
Learning Outcomes
- Analyze continuous- and discrete-time signals in time and frequency domains
- Apply Fourier, Laplace, and Z-transforms to engineering systems
- Design and characterize LTI systems
Prerequisites
MATH 201
OER Source & License
MIT OCW 6.003 (CC BY-NC-SA)
Introduction to Machine Learning
Supervised and unsupervised learning, regression, classification, neural networks, evaluation metrics, bias-variance tradeoff, and ethical considerations in ML deployment.
Learning Outcomes
- Implement supervised and unsupervised learning algorithms
- Apply model evaluation, regularization, and hyperparameter tuning
- Explain ethical implications of machine learning systems
Prerequisites
CS 201, MATH 211
OER Source & License
LibreTexts ML resources (CC BY); original authored content
Graduate Courses
Advanced Distributed Systems
Graduate-level study of distributed systems design: CAP theorem, consensus (Paxos, Raft), distributed transactions, fault tolerance, consistency models, and case studies of production systems.
Learning Outcomes
- Analyze distributed consensus algorithms and their fault-tolerance properties
- Design and evaluate distributed storage and replication systems
- Conduct and present a literature-reviewed distributed systems study
Prerequisites
CS 301, CS 311
OER Source & License
MIT OCW 6.824 lecture notes (CC BY-NC-SA) — license review required
Research Methods in Engineering
Introduction to scientific and engineering research: literature review, hypothesis formulation, experimental design, quantitative and qualitative methods, academic writing, citation standards, and research ethics.
Learning Outcomes
- Formulate a research question and hypothesis in an engineering domain
- Design an experimental methodology and select appropriate evaluation metrics
- Produce a structured research proposal with bibliography and ethical review
Prerequisites
Graduate standing
OER Source & License
Original authored content — no copyrighted base text
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