Bachelor of Science in Artificial Intelligence
Key Facts
| Section | Details |
|---|---|
| Award | Bachelor of Science (BSc) in Artificial Intelligence |
| Partner Institution | Illinois Institute of Technology |
| Level | Undergraduate |
| Duration | 4 Years (8 Terms) |
| Format | Full-time |
| Credit Definition | 1 credit = 1 hour of tuition/lab/recitation of a particular subject per week for minimum 10 weeks to maximum 12 weeks (3 months). |
| Maximum Total Credits Per Term | \(4 \text{ days } \times 4 \text{ slots a day } \times \text{ 1.5 hours per slot } = 24 \text{ credits }.\) |
| Total Credits For The Program | 101 credits (Tetr) + 21 credits (Illinois Tech Online) + 30 credits (Illinois Tech) = 152 credits. |
| Start Time | Fall Semester |
| Study Locations | Multi-country immersive program (Dubai, India, Singapore, China, Europe, USA + flexible global incubation term) |
| Program Goal | Train AI builders and founders, not just AI engineers. Students graduate having shipped products, worked with real users, and understood both technical depth and business realities. |
Maximum Slots Per Term. Total slots available = \(12 \text{ weeks } \times 5 \text{ days } \times 4 \text{ slots a day } = 240.\) This leaves 4 weeks \((20 \text{ working days})\) for exams & immersions. However, we can spare \(12\) more days for holidays, immersions, masterclasses, etc. So we reduce the workload to \(4\) days a week. Total usable slots = \(12 \text{ weeks } \times 4 \text{ days } \times 4 \text{ slots a day } = 192.\)
Program Details
| Section | Details |
|---|---|
| Program Philosophy | Most AI programs teach theory first and hope innovation follows. This program flips that. You build first, confront reality early, and develop technical depth alongside entrepreneurial instinct. The goal isn’t academic completeness; it’s the ability to build, ship, scale, and fund AI products responsibly. |
| Learning Model | Every term ends with a shipped product or measurable outcome. Students repeatedly experience the full loop: idea → build → deploy → users → feedback → iteration. This develops execution confidence early. |
| Technical Foundation | Strong grounding in mathematics, machine learning, systems design, and deployment economics. Theory is introduced when it becomes useful, then deepened later so understanding stays practical but rigorous. |
| Business Integration | Business thinking isn’t taught in isolation. Pricing, infrastructure cost, CAC, compliance, and product-market fit are embedded directly into technical coursework. |
| Global Exposure | Students experience multiple technology ecosystems: emerging markets, manufacturing hubs, financial centers, and startup ecosystems. The aim is to build founder intuition about how markets actually differ. |
| Specialization Philosophy | AI specialization is intentionally delayed. Students first learn foundations across systems, ML, deployment, and business. Specialization happens once exposure makes decisions informed rather than premature. |
Curriculum Structure
| Term | Location | What You'll Learn | What You'll Do |
|---|---|---|---|
| 1 | Dubai | Linear algebra, calculus, Python, full-stack web dev, sales & marketing, economics | Build an AI Agent That Autonomously Runs A Micro-Business: an AI agent that operates autonomously and generates measurable economic output |
| 2 | India | Multivariate calculus, physics, data structures, data engineering, practical ML, macroeconomics, AI ethics | Crack Your First Enterprise Deal: a ML/AI system with 99.9% uptime, 1,000+ req/sec, full security audit, and complete audit logs |
| 3 | Shanghai | Robotics physics, classical AI, enterprise cloud & DevOps, computer vision & edge AI, hardware prototyping, web development, project management | Build Your Own Smart Wearable: an AI hardware product prototyped, manufactured in Shenzhen (100 units), and sold (50+ units) |
| 4 | Ghana | Probability & statistics, system design, deep learning, computer architecture & edge computing, business metrics & fundraising, nutrition & wellness, data networks | Deploy an AI System That Measurably Improves Lives: a domain-specific AI system built in partnership with a Ghanaian institution, with real outcome data |
| 5 | Europe | FinTech: Stochastic calculus, algorithmic trading, financial regulation, risk management HealthTech: Computational biology, medical imaging AI, health data & regulation, AI for scientific discovery Consumer AI: Recommender systems, foundation models, behavioral psychology, social network analysis, viral growth |
Break Into A Specialized Industry: a domain-specific product with real compliance, live users, or revenue, pitched to domain investors |
| 6 | Global | (A) Incubation: Fundraising, accelerator applications, GTM strategy (B) Internship: Shipping real features at a high-growth AI startup | Build A Startup YC Style: 1,000+ users OR $10K+ revenue OR $50K+ funding OR YC/Techstars acceptance |
| 7–8 | United States | Mathematical modeling, data mining, AI & society, depth electives (NLP, computer vision, robotics, probabilistic models), minor electives, IPRO project | Seek Funding & Scale: Scale your venture. Finish your degree. |
Program Benefits
| Category | Details |
|---|---|
| Build Real Products | Students graduate with a portfolio of deployed AI products, not just assignments. |
| Global Industry Exposure | Study across multiple tech ecosystems to understand both developed and emerging markets. |
| Founder-Oriented Training | Emphasis on product thinking, customer discovery, and sustainable business models. |
| Technical Depth That Matters | Focus on ML systems, deployment, compute economics, data pipelines, and real-world constraints. |
| Startup Incubation Opportunity | Dedicated term for building a startup or working in a high-growth AI company. |
| Career Optionality | Graduates can pursue startups, AI engineering roles, research pathways, or further study. |
| Industry Network Access | Exposure to investors, founders, engineers, and operators across multiple global hubs. |
Is This Program Right For You?
| FAQ | Answer |
|---|---|
| Do I need prior programming experience? | No. Students start from fundamentals, but curiosity about technology is essential. |
| Is strong mathematics required? | Comfort with mathematics helps. The program builds the necessary mathematical foundation progressively. |
| Is this more technical or business focused? | Both. The philosophy is that AI founders must understand the technology deeply while thinking commercially. |
| Is it research-oriented? | Primarily product and industry oriented, but strong academic pathways remain open. |
Career Pathways
| Path | Examples |
|---|---|
| AI Product Founder | SaaS AI tools, AI infrastructure startups, vertical AI solutions |
| AI Engineer / ML Engineer | Applied ML, AI deployment, data engineering |
| Domain Specialist | FinTech AI, Health AI, Robotics, Consumer AI |
| Graduate Study | AI, Data Science, Computational Sciences, Entrepreneurship |
* indicates a common BMT course
** indicates course provided virtually by Illinois Tech
Term 1: Dubai
Build your first AI product and ship it to real users: all in 16 weeks. This may look ambitious for Term 1, but Dubai itself went from desert to global city in one generation! It's a proof that ambitious things can be built fast. A place, where UG"world's tallest building" and "indoor ski slope in the desert" are normal, is perhaps the best place to make ambition your default setting.
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 101 | Build an AI Agent That Autonomously Runs A Micro-Business | Design, deploy, and iterate on an AI agent that independently manages a revenue-generating micro-business: handling customer acquisition, operations, decision-making, and optimization. Acquire at least 1,000 active users or generate $10,000 in revenue through autonomous AI-driven operations. | - | 1 + 3 | 7 (10) + Self-Paced. |
| MATH 101 | Linear Algebra for Machine Learning | Learn vectors, matrices, transformations: the mathematical language of AI. Understand what happens inside neural networks geometrically. Learn just enough to start building. | How Do Machines Actually "See" Data? | 3 | 20 (30) |
| MATH 102 | Calculus for Machine Learning | Derivatives, gradients, optimization: understand how models learn. Backpropagation from scratch. Focus on computational efficiency and resource-constrained optimization. | How Does an AI Know When It's Wrong and Get Better? | 3 | 20 (30) |
| CS 101 | Introduction to Computational Thinking | Learn to break down problems algorithmically and implement solutions in Python. Focus on writing clean, maintainable code for data processing and simple ML workflows. Emphasis on shipping working code quickly. | How Do You Break Any Problem Down Until a Machine Can Solve It? | 3 + 1 | 20 (30) + 7 (10) |
| CS 102 | Rapid Web Development for AI Products | Build full-stack web apps that can serve ML models using modern frameworks. Next.js/React, FastAPI, Vercel/Railway deployment. Ship to production in hours, not weeks. | How Do You Turn Models Into Real Apps Fast? | 3 | 20 (30) |
| BUS 101* | Sales & Marketing | Understand how technology products find customers. Learn positioning, customer discovery, digital marketing funnels, performance ads (Meta/Google), and early-stage sales tactics for AI products. Focus on validating demand quickly rather than theoretical marketing plans. | How Do You Make People Want Your Product? | 3 | 20 (30) |
| BUS 102* | Economics | Introduces economic thinking for builders: demand, incentives, pricing, market structure, and unit economics. Focus on interpreting markets and evaluating opportunities. | How Do Markets Decide What Wins? | 2 | 14 (21) |
| Credits | Slots | Hours |
|---|---|---|
| 22 | 128 | 192 |
| Technical | Business | Capstone | Illinois Tech |
|---|---|---|---|
| 59% | 23% | 18% | 0% |
Term 2: India
Learn to build AI that works for everyone, everywhere. The focus is to learn how to deal with real-world data and scale the product despite messy constraints. India invented "frugal innovation" out of necessity: learn from engineers who build $50 solutions for $5,000 problems. This is exactly what a startup founder should know: how to optimize costs ruthlessly while still developing world-class products!
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 102 | Crack Your First Enterprise Deal | Build a production-ready ML/AI solution solving a real operational problem for a business, such as demand forecasting, fraud detection, automation, or predictive analytics. Pitch, negotiate, and close your first B2B contract. Secure at least 1 paying enterprise client and deploy the solution in a live business environment. | - | 1 + 3 | 7 (10) + Self-Paced. |
| MATH 103 | Multivariate Calculus for Machine Learning | Extends calculus to multiple variables: partial derivatives, gradients, Jacobians, constrained optimization. Understand optimization of high-dimensional loss surfaces, and parameter tuning. | How do AI models optimize across millions of parameters simultaneously without collapsing? | 2 | 14 (21) |
| MATH 104 | General Physics | Build the physical intuition via mechanics, electricity, magnetism & atomic physics, needed to reason about sensors, hardware, energy, and real-world systems that AI ultimately interacts with. | How Do the Laws of Nature Become the Laws of Technology? | 4 | 27 (40) |
| CS 103 | Data Structures & Algorithm Design | Understand the common concepts of arrays, trees, graphs, hash tables, time and space complexity. Write efficient code that works on limited hardware. Optimize for memory and CPU constraints. | How Do the World's Fastest Programs Actually Work? | 3 | 20 (30) |
| CS 104 | Data Engineering for Emerging Markets | Understand databases, SQL, data cleaning, ETL pipelines, working with APIs. Handle messy, multilingual, sparse data. Build pipelines that work with intermittent connectivity and limited infrastructure. | How Do You Engineer Data Systems for the World's Messiest Realities? | 3 | 20 (30) |
| CS 105 | Practical Machine Learning | Hands-on ML: regression, classification, decision trees, random forests. Use scikit-learn to build predictive models. Deploy models quickly using pre-trained solutions and transfer learning. | How did Netflix save $1 billion per year with an algorithm and why couldn't Blockbuster copy it? | 3 | 20 (30) |
| BUS 103* | Global Macroeconomics | Introduces macroeconomic forces shaping technology markets: inflation, interest rates, trade flows, demographics, policy, and technological diffusion. Focus on how macro trends affect startup strategy, capital access, and product-market timing in emerging economies. | How to understand economic forces that shape the world and the economics behind the startup ecosystem? | 3 | 20 (30) |
| PHIL 381** | Artificial Intelligence, Philosophy and Ethics | Examines ethical, philosophical, and societal implications of AI: bias, fairness, privacy, autonomy, and human-AI collaboration. Case studies from real deployments, especially in developing economies. Focus on responsible innovation rather than abstract theory. | How Do We Control Intelligence We Don’t Fully Understand? | 3 | Virtual |
| Credits | Slots | Hours |
|---|---|---|
| 25 | 128 | 192 |
| Technical | Business | Capstone | Illinois Tech |
|---|---|---|---|
| 60% | 12% | 16% | 12% |
Term 3: Shanghai
Stop building software that lives in browsers. Start building AI that moves, sees, and exists in the physical world. Shenzhen = hardware capital of the world. It's 2-hour train ride from Shanghai where iPhones, drones, and electric cars are manufactured at scale. Prototype on Monday, 100 units on Friday at $2/unit: experience hardware iteration speeds and costs impossible anywhere else in the world. WeChat super-app, Alibaba Cloud, DJI drones, China's surveillance infrastructure, smart cities, IoT deployments: see how China builds tech differently from Silicon Valley.
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 201 | Build Your Own Smart Wearable. | Design and prototype an AI-powered hardware product, such as a smart camera, voice assistant device, robotics system, or IoT sensor platform. Integrate edge AI, embedded systems, and hardware-software optimization. Build a fully functional prototype and secure at least 100 pre-orders, pilot users, or signed letters of intent. | - | 1 + 3 | 7 (10) + Self-paced. |
| MATH 202 | Physics of Robots | Understand mathematical modeling of robotic motion and perception. Connect physics (mechanics, control systems, sensors, actuators, signal processing and feedback loops) directly to robots. | How Do You Build Machines That Interact With The Real World? | 3 | 20 (30) |
| CS 204 | Classical Artificial Intelligence | Foundations of symbolic AI: search algorithms, planning, constraint satisfaction, knowledge representation, expert systems, probabilistic reasoning. Complements modern ML by understanding when rules outperform neural networks. | How Were the Rules of Intelligence Written Before Anyone Had Enough Data? | 3 | 20 (30) |
| CS 203 | Enterprise Cloud Architecture & DevOps | AWS/GCP/Azure for enterprise. Containerization (Docker/Kubernetes), CI/CD, infrastructure as code, security compliance, multi-region deployment. SOC2, ISO27001 considerations. | How Do You Build Software That Never Goes Down? | 3 | 20 (30) |
| CS 206 | Computer Vision & Embedded AI | CNNs, object detection, segmentation, tracking. Deploy models on edge devices (Raspberry Pi, Jetson Nano, mobile phones). Real-time inference, camera interfaces, video processing pipelines. | How Do You Teach a Tiny Chip to See the World? | 3 + 1 | 20 (30) + 7 (10) |
| BUS 202 | Hardware Prototyping & Manufacturing at Scale | Work with Shenzhen manufacturers. PCB design, sensor integration, enclosure design, rapid prototyping. MOQs, unit economics, supply chain, quality control, supply chain risk, tariffs, international shipping. Factory visits. | How Do You Turn Ideas Into Mass-Produced Hardware? | 2 | 14 (20) |
| ITMD 361** | Fundamentals of Web Development | Reinforces modern web fundamentals: responsive design, performance optimization, accessibility, security basics, and maintainable front-end architecture. Positioned as consolidation rather than introduction, ensuring production-grade web maturity. (We've already covered this btw.) | How Do You Build the Front Door of Every Digital Product in the World? | 3 | Virtual |
| ITMM 471** | Project Management for Information Technology and Management | Practical project management for technical teams: Agile, Scrum, milestone planning, stakeholder communication, risk management, compliance documentation, and cross-functional coordination for enterprise deployments. | How Do Tech Leaders Coordinate Chaos and Keep Tech Projects on Track? | 3 | Virtual |
| Credits | Slots | Hours |
|---|---|---|
| 25 | 108 | 162 |
| Technical | Business | Capstone | Illinois Tech |
|---|---|---|---|
| 48% | 12% | 16% | 24% |
Term 4: Ghana
Learn to build systems that are reliable, secure, and built for the people who need them most. Ghana is Africa's technology gateway: one of the continent's most stable democracies, home to a booming tech ecosystem, and a place where real infrastructure gaps mean AI can create disproportionate impact.
Here, you stop optimizing for engagement metrics and start optimizing for human outcomes. You'll encounter the hardest version of the software problem: building systems that work when the power is unreliable, the internet is slow, the data barely exists, and the stakes (health, livelihoods, education) are real. If you can build something here that works and matters, you can build anything anywhere.
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 202 | Deploy an AI System That Measurably Improves Lives | Select one domain such as healthcare, agriculture, education, or financial access, and co-build an AI solution with a Ghanaian institutional partner. Focus on real deployment, usability, and measurable social impact. Improve outcomes for at least 500 beneficiaries and demonstrate quantifiable impact metrics validated by the partner institution. | - | 1 + 3 | 7 (10) + Self-paced. |
| MATH 201 | Probability & Statistical Inference | Understand distributions, hypothesis testing, Bayesian reasoning, rigorous uncertainty quantification, risk modeling and confidence intervals for high-stakes decisions. | How Do You Make Confident Decisions With Incomplete Information? | 3 | 20 (30) |
| CS 201 | System Design for Mission-Critical Applications | Design highly available, fault-tolerant systems. Load balancing, caching, distributed databases, disaster recovery. Build for 99.99% uptime. Circuit breakers, graceful degradation. | How does Google search 40 billion web pages in 0.2 seconds, and Netflix Stream to 200 Million People at the Same Time? | 2 | 14 (20) |
| CS 202 | Practical Deep Learning | Neural networks, CNNs, RNNs, training dynamics. Build models from scratch in PyTorch. Focus on reproducibility, versioning, and production-grade training pipelines. | How do machines learn to recognize patterns humans struggle to even describe? | 4 | 27 (40) |
| CS 205 | Computer Architecture & Edge Computing | How CPUs/GPUs/TPUs/NPUs actually work. Memory hierarchies, parallel processing, ARM vs x86. Edge AI chips, model optimization for specific hardware. Power consumption analysis. | How Does Hardware Shape the Speed of Intelligence? | 3 | 20 (30) |
| BUS 201* | Business Metrics Analysis & Start-up Fundraising | Unit economics, cohort analysis, CAC/LTV, burn rate, runway planning, pricing strategy, financial modeling, KPI dashboards, data-driven growth, and investor readiness. Term sheets, dilution, cap tables, valuation methods, and pitch construction. Translate technical value into business outcomes and credible forecasts. | How Do You Make Sure Your Startup Is Worth a Million Dollars? | 3 | 20 (30) |
| FDSN 201** | Nutrition & Wellness | Foundations of nutrition, sleep science, stress management, and cognitive performance. Focus on sustaining energy, mental clarity, and resilience for high-intensity technical careers and startup environments. | How Does What You Eat Change How Well You Think? | 3 | Virtual |
| ITMO 340** | Introduction to Data Networks and the Internet | Internet architecture, networking protocols, wireless communication, latency, bandwidth optimization, IoT connectivity, and distributed system communication. Focus on implications for AI systems deployed globally and on edge devices. | How Does a Message Travel From Your Phone to the Other Side of the Planet in Milliseconds? | 3 | Virtual |
| Credits | Slots | Hours |
|---|---|---|
| 25 | 108 | 162 |
| Technical | Business | Capstone | Illinois Tech |
|---|---|---|---|
| 48% | 12% | 16% | 24% |
Term 5: Europe
Stop being a generalist. Go deep in FinTech, HealthTech, ConsumerTech, or the frontier that will define your career. Europe is at the frontier of the healthcare systems, climate leadership, finance, pharma and automotive industries. If you can build compliant products here (GDPR, AI Act, medical device directives, etc.), you can build them anywhere in the world.
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 301 | Break Into A Specialized Industry (FinTech) | Build a fintech product (algorithmic trading system, payment processor, RegTech tool). Must handle real money (even if small amounts), comply with relevant regulations, demonstrate security audit. Present to fintech VCs. | - | 1 + 3 | 7 (10) + Self-paced |
| MATH 301 | Stochastic Calculus & Financial Mathematics | Understand Brownian motion, Ito's lemma, Black-Scholes, option pricing. Stochastic differential equations for modeling financial systems. | How Does Math Beat the Stock Market? | 3 | 20 (30) |
| CS 301 | Quantitative Trading | Time series forecasting, reinforcement learning for trading, portfolio optimization, risk modeling, high-frequency trading signals. Backtesting frameworks. | How Do Quants Use Math to Predict Unpredictable Markets? | 3 | 20 (30) |
| FIN 301 | Financial Regulation & Compliance Tech | MiFID II, GDPR, AML/KYC requirements, regulatory reporting. Building compliant fintech systems. RegTech tools and frameworks. | How do fintech companies innovate with money while staying compliant with laws designed to control it? | 3 | 20 (30) |
| FIN 302 | Building & Scaling FinTech Products | Payment processing, banking partnerships, licensing requirements, fraud prevention. Unit economics of fintech. Case studies: Stripe, Wise, Revolut. | How did Stripe become a $95 billion company by making payments "just work"? | 3 | 20 (30) |
| FIN 303 | Hedging & Risk Management | Learn to identify, measure and mitigate financial, operational, and technological risks, with practical strategies founders use to protect businesses against uncertainty while preserving growth opportunities. | How Do You Protect a Billion-Dollar Bet From Going Wrong? | 3 | 20 (30) |
| ITMD 321** | Data Modeling and Applications | Principles of data modeling across relational, document, graph, and time-series systems. Schema design, normalization, query optimization, and data lifecycle management. Emphasis on building scalable, maintainable data foundations for modern software systems. | How do well-designed data structures determine whether a system scales smoothly or collapses under growth? | 3 | Virtual |
| MATH 574** | Bayesian Computational Statistics | Computational Bayesian methods including MCMC, variational inference, probabilistic modeling, and uncertainty estimation. Focus on practical inference techniques for complex real-world datasets across multiple domains. | How can you update beliefs rationally when new data arrives instead of retraining everything from scratch? | 3 | Virtual |
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 302 | Break Into A Specialized Industry (HealthTech) | Build a medical AI product (diagnostic tool, clinical decision support, patient monitoring system). Get IRB approval for a small pilot study. Demonstrate HIPAA compliance. Present clinical validation results. Pitch to healthtech investors. | - | 1 + 3 | 7 (10) + Self-Paced |
| CS 302 | Computational Biology & Genomics | DNA/RNA sequencing analysis, protein structure prediction, genomic data processing. BLAST, alignment algorithms, variant calling. | How Is AI Rewriting What We Know About Human Biology? | 3 | 20 (30) |
| CS 303 | Medical Imaging & Diagnostic AI | Deep learning for radiology, pathology, dermatology. Segmentation, classification, detection. Working with DICOM, handling class imbalance, clinical validation. | How Does AI Learn to Spot Cancer Better Than a Doctor? | 3 | 20 (30) |
| CS 304 | Health Data Privacy, Device Regulation & Clinical Trials | FHIR standards, HL7, EHR integration, de-identification, federated learning, differential privacy. Working with sensitive health data at scale. FDA/CE/NMPA approval processes, clinical trial design, HIPAA/GDPR compliance, medical device classification, post-market surveillance. | How do you prove that an AI system is safe enough to make decisions about human health? | 3 | 20 (30) |
| CS 305 | AI for Scientific Discovery | Protein folding (AlphaFold), drug discovery, materials science, theorem proving, mathematical reasoning. ML for differential equations, scientific simulation. | How Is AI Doing Science Faster Than Any Human Ever Could? | 3 | 20 (30) |
| CS 306 | Agricultural Biotechnology & Data | CRISPR, gene editing for crops, molecular breeding, genomic selection. Managing field trial data. Regulatory approval for GMOs. | How did scientists create rice that produces Vitamin A and could prevent blindness in millions of children? | 3 | 20 (30) |
| ITMD 321** | Data Modeling and Applications | Principles of data modeling across relational, document, graph, and time-series systems. Schema design, normalization, query optimization, and data lifecycle management. Emphasis on building scalable, maintainable data foundations for modern software systems. | How do well-designed data structures determine whether a system scales smoothly or collapses under growth? | 3 | Virtual |
| MATH 574** | Bayesian Computational Statistics | Focus on practical inference techniques for complex real-world datasets via computational Bayesian methods including MCMC, probabilistic modeling, and uncertainty estimation. | How can you update beliefs rationally when new data arrives instead of retraining everything from scratch? | 3 | Virtual |
| Course Code | Course Name | Course Overview | Core Understanding | Credits | Total Slots (Hours) |
|---|---|---|---|---|---|
| CAPSTONE 303 | Break Into A Specialized Industry (ConsumerTech) | Build a consumer AI product (social app, content platform, personalized service). Launch publicly. Achieve 1,000+ MAU with 40%+ D7 retention. Demonstrate healthy engagement metrics (not just addictive). Present growth analysis and ethical considerations. | - | 1 + 3 | 7 (10) + Self-paced |
| CS 307 | Recommender Systems & Personalization | Collaborative filtering, content-based filtering, hybrid approaches. Cold start problem, explore-exploit, diversity-accuracy tradeoffs. Algorithms behind TikTok, YouTube, Spotify. | How Do You Build an Algorithm That Knows You Better Than You Know Yourself? | 3 | 20 (30) |
| CS 308 | Foundation Models & Large-Scale Training | Transformer architecture deep dive, distributed training, mixture of experts, RLHF, constitutional AI. Working at scale: ZeRO, pipeline parallelism, activation checkpointing. | How Were GPT, Claude, and Gemini Actually Built? | 3 | 20 (30) |
| CS 309 | Behavioral Psychology & Persuasive Design | Cognitive biases, habit formation, attention economics, persuasive technology. Ethics of addictive design. Designing for behavior change. | Why do people spend 3+ hours daily on apps they claim to hate? Can we deliberately design addictive apps? | 3 | 20 (30) |
| CS 310 | Social Network Analysis | Learn to detect communities, influence, centrality, and cascades. You'll learn how small design choices reshape entire networks. | How Do You Map the Hidden Structure of Human Relationships on Social Media? | 3 | 20 (30) |
| BUS 302 | Consumer Growth & Viral Mechanics | Product-led growth, viral loops, network effects, growth hacking, A/B testing, retention optimization. Case studies: Instagram, Snapchat, Discord. | Why did Clubhouse get to 10M users in months then die, while Discord quietly built to 150M users over years? | 3 | 20 (30) |
| ITMD 321** | Data Modeling and Applications | Principles of data modeling across relational, document, graph, and time-series systems. Schema design, normalization, query optimization, and data lifecycle management. Emphasis on building scalable, maintainable data foundations for modern software systems. | How do well-designed data structures determine whether a system scales smoothly or collapses under growth? | 3 | Virtual |
| MATH 574** | Bayesian Computational Statistics | Computational Bayesian methods including MCMC, variational inference, probabilistic modeling, and uncertainty estimation. Focus on practical inference techniques for complex real-world datasets across multiple domains. | How can you update beliefs rationally when new data arrives instead of retraining everything from scratch? | 3 | Virtual |
| Credits | Slots | Hours |
|---|---|---|
| 25 | 107 | 160 |
| Technical | Business | Capstone | Illinois Tech |
|---|---|---|---|
| 48% | 12% | 16% | 24% |
Term 6: Global
No professors. No grades. Just you, your product, and users who don't care about your status quo. Get incubated and try to build a real company OR get in an established startup/company to learn about the industry. This is the term where entrepreneurship is the most serious! You can build anywhere. The whole world is your market.
Build your own startup. Get into an accelerator (ideally in your specialization domain). Raise pre-seed funding. Get real users/customers.
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Work at an AI startup/company (preference for high-growth startups, not Big Tech). Ship real features. See how professional teams work. Experience fast-paced startup environment.
Success Metrics (for both tracks)
- Ship something used by 1,000+ people, OR
- Generate $10,000+ in revenue, OR
- Raise $50,000+ in funding, OR
- Get accepted to YC/Techstars/top accelerator
Location: Flexible (can be in US, Europe, India, China, or student's home country depending on opportunity).
Term 7: United States
This term you focus on your academics; and in parallel try to continue working on your incubated company. You can try to get funding as well. Nowhere else do founders casually discuss term sheets over coffee. Your batch-mate's roommate knows someone at Sequoia: the "six degrees of separation" is more like two degrees here. You'll have access to people who built GPT, AlphaGo, and self-driving cars!
| Course Code | Course Name | Course Overview | Core Understanding | Credits |
|---|---|---|---|---|
| MATH 486 | Mathematical Modeling I | The course covers modeling applications from physics, engineering, biology, business. Understand higher dimensional analysis, scaling, analytic and computational tools. | How can simple mathematical models predict complex real-world systems surprisingly well? | 3 |
| CS 422 | Data Mining | This course covers market basket analysis, nearest neighbor, decision trees, clustering, implementing different techniques of these algorithms. | How do companies discover hidden patterns in data you didn’t even realize you were creating? | 3 |
| ELECTIVE 1 | AI Breadth Course | < Pick one from the options given below. > | - | 3 |
| ELECTIVE 2 | AI Technical Course | < Pick one from the options given below. > | - | 3 |
| ELECTIVE 3 | Minor Elective 1 | University-specific elective as per the options provided then. | - | 3 |
Term 8: United States
Your focus on academics and scaling up your business continues.
| Course Code | Course Name | Course Overview | Core Understanding | Credits |
|---|---|---|---|---|
| CS 485 | Computers and Society | Discussion of the impact of computer technology on present and future society. Historical development of the computer. Social issues raised by cybernetics. | Are we shaping technology or is technology quietly reshaping how we think, work, and live? | 3 |
| ELECTIVE 4 | AI Depth Course | < Pick one from the options given below. > | - | 3 |
| ELECTIVE 5 | AI Technical Elective | < Pick one from the options given below. > | - | 3 |
| ELECTIVE 6 | Minor Elective 2 | University-specific elective as per the options provided then. | - | 3 |
| PROJECT 1 | IPRO Elective | Practical interdisciplinary project to be submitted to the university. | 3 |
ELECTIVE 1: AI Breadth Options
| Course Code | Course Name | Course Overview | Core Understanding |
|---|---|---|---|
| PSYC 423 | Learning Theory | Explore how humans and machines learn across psychology, neuroscience, education, and AI. Covers major learning theories, motivation, cognition, technology, and how intelligence and disability affect learning processes. | How do humans (and AI) actually learn; and why do some methods work better than others? |
| PSYC 426 | Cognitive Science | An interdisciplinary look at how the mind works, combining psychology, neuroscience, computer science, linguistics, and behavioral economics to understand perception, reasoning, and intelligence. | How does the mind turn raw sensory input into thoughts, decisions, and intelligence? |
ELECTIVE 2 & 5: AI Technical Course Options (Pick Two)
| Course Code | Course Name | Course Overview | Core Understanding |
|---|---|---|---|
| CS 513 | Geospatial Vision and Visualization | Learn how digital maps, GPS, and location-based services are built using computer vision, sensing, and visualization. Covers mapping technologies, spatial data processing, and real-world geospatial applications. | How do Google Maps-like systems know exactly where you are and what’s around you? |
| CS 528 | Data Privacy and Security | Study how organizations use data without compromising privacy. Covers cryptography, secure computation, anonymization, and privacy-preserving technologies across sectors like social networks, finance, and cloud systems. | How can companies use your data without actually exposing your identity? |
| CS 539 | Game Theory: Algorithms and Applications | Explore algorithmic game theory, Nash equilibria, market dynamics, and network decision-making. Applications include economics, internet systems, and strategic interactions in complex networks. | How do competing players make “rational” decisions, and why do markets sometimes still behave irrationally? |
| CS 549 | Cryptography | Foundations of secure digital communication: encryption, signatures, key exchange, authentication, and privacy protocols. Emphasis on mathematical principles and modern security applications. | How can two people exchange secrets online when everyone else is listening? |
| CS 552 | Distributed Real-Time Systems | Study systems where correctness depends on both accuracy and timing. Covers distributed computing, embedded systems, and real-time applications like smart vehicles and automated infrastructure. | How do critical systems make the right decision at exactly the right time? |
| CS 553 | Cloud Computing | Design scalable distributed systems using modern cloud platforms. Covers resource management, big-data frameworks, distributed infrastructure, and real-world cloud deployments. | How can a tiny startup suddenly serve millions of users overnight? |
| CS 579 | Online Social Network Analysis | Analyze social networks using graph theory, text analytics, and AI. Covers influence, communities, sentiment analysis, and applications in politics, marketing, and public health. | How does a single post sometimes influence millions of people? |
| CS 581 | Advanced Artificial Intelligence | Deep dive into advanced AI topics: optimization, probabilistic reasoning, reinforcement learning, deep learning, decision-making under uncertainty, and AI ethics and safety. | How do intelligent systems make complex decisions when the world is uncertain? |
| CS 582 | Computational Robotics | Algorithms for sensing, perception, navigation, and decision-making in robots. Covers sensor fusion, localization, motion planning, and multi-agent coordination. | How do robots navigate messy real-world environments without constant human control? |
| MATH 481 | Introduction to Stochastic Processes | Learn probabilistic models like Markov chains, Poisson processes, and Brownian motion for modeling randomness in finance, engineering, and natural systems. | How can randomness itself be modeled and predicted? |
| MATH 484 | Regression | Statistical modeling using linear and generalized regression, hypothesis testing, and experimental design to extract insights and make predictions from data. | How do we turn messy data into reliable predictions? |
| MATH 487 | Mathematical Modeling II | Cover advanced modeling techniques including queuing theory, financial derivatives, and interpreting mathematical solutions for real-world decision making. | How can math predict complex systems like markets, traffic, or customer behavior? |
ELECTIVE 4: AI Depth Options
| Course Code | Course Name | Course Overview | Core Understanding |
|---|---|---|---|
| MATH 569 | Statistical Learning | Learn modern data-driven methods like regression, classification, kernel methods, and SVMs, with mathematical behind-the-scenes, to extract patterns and make predictions from large datasets. | How do machines turn raw data into surprisingly accurate predictions? |
| CS 512 | Computer Vision | Foundations of computer vision including image formation, feature extraction, object detection, motion estimation, and deep learning-based visual understanding. | How do machines learn to “see” and interpret the visual world like humans? |
| CS 578 | Interactive and Transparent Machine Learning | Explore human-AI collaboration, explainable AI, bias analysis, interactive learning systems, and transparency in machine learning decision-making. | How AI decisions become understandable enough that humans actually trust them? |
| CS 583 | Probabilistic Graphical Models | Learn graphical models like Bayesian networks, HMMs, and CRFs for reasoning under uncertainty across domains like vision, NLP, and healthcare. | How can complex uncertain systems be modeled in a way humans can interpret? |
| CS 585 | Natural Language Processing | Study how machines process human language: parsing, semantics, text generation, information retrieval, and conversational AI applications. | How do machines learn to understand and generate human language convincingly? |
| ECE 442 | Internet of Things and Cyber Physical Systems | Fundamentals of IoT systems: sensors, wireless protocols, embedded computing, data fusion, privacy, and real-world cyber-physical applications. | How do everyday objects become intelligent and connected to the digital world? |
* indicates course not considered for credit mapping
** indicates course provided virtually by Illinois Tech
( ) indicates optional course offered by Illinois Tech
127 credits are required for Illinois Tech UG degree, of which 21 (online) + 30 (last 2 terms) are taken care by Illinois Tech. So, Tetr needs to cover 76 credits.
| Tetr Term | Course Code | Course Name | Credits | Mapped Course Code | Mapped Course Name | Illinois Tech Term | Mapped Credits |
|---|---|---|---|---|---|---|---|
| 1 | MATH 101 | Linear Algebra for Machine Learning | 3 | MATH 332 | Elementary Linear Algebra | 4 | 3 |
| 1 | MATH 102 | Calculus for Machine Learning | 3 | MATH 151 | Calculus I | 1 | 5 |
| MATH 152 | Calculus II | 2 | 5 | ||||
| 1 | CS 101 | Introduction to Computational Thinking | 4 | CS 100 | Introduction to Profession | 1 | 2 |
| CS 115 | Object-Oriented Programming I | 1 | 2 | ||||
| CS 340 | Programming Paradigms and Patterns | 4 | 3 | ||||
| 1 | CS 102 | Rapid Web Development for AI Products | 3 | CS 116 | Object-Oriented Programming II | 2 | 2 |
| 1 | BUS 101 | Sales & Marketing | 3 | - | Humanities 200-level Course | 1 | 3 |
| 1 | BUS 102 | Economics | 2 | - | Social Sciences Elective | 1 | 3 |
| 2 | MATH 103 | Multivariate Calculus | 2 | MATH 251 | Multivariate and Vector Calculus | 3 | 4 |
| 2 | MATH 104 | General Physics | 4 | PHYS 123 | General Physics I: Mechanics | 2 | 4 |
| PHYS 221 | General Physics II: Electricity and Magnetism | 3 | 4 | ||||
| 2 | CS 103 | Data Structures & Algorithm Design | 3 | CS 330 | Discrete Structures | 3 | 3 |
| CS 331 | Data Structures and Algorithms | 3 | 3 | ||||
| CS 430 | Introduction to Algorithms | 4 | 3 | ||||
| 2 | CS 104 | Data Engineering for Emerging Markets | 3 | CS 425 | Database Organization | 5 | 3 |
| 2 | PHIL 381** | Artificial Intelligence, Philosophy and Ethics | 3 | - | Humanities 300 Elective | 2 | 3 |
| 3 | MATH 202 | Physics of Robots | 3 | - | Science Elective | 6 | 3 |
| 3 | CS 204 | Classical Artificial Intelligence | 3 | CS 480 | Introduction to Artificial Intelligence | 5 | 3 |
| 3 | BUS 202 | Hardware Prototyping & Manufacturing at Scale | 2 | - | Social Science Elective | 5 | 3 |
| 3 | ITMD 361** | Fundamentals of Web Development | 3 | - | Replacement of Social Science 300 Elective | 2 | 3 |
| 3 | ITMM 471** | Project Management for Information Technology and Management | 3 | - | Replacement of Social Science 300 Elective | 3 | 3 |
| 4 | MATH 201 | Probability & Statistical Inference | 4 | MATH 474 | Probability and Statistics | 5 | 3 |
| 4 | CS 201 | System Design for Mission-Critical Applications | 3 | CS 487 | Software Engineering I | 6 | 3 |
| 4 | CS 202 | Practical Deep Learning | 4 | (CS 577) | AI Technical Elective (Deep Learning) | 6 | 3 |
| 4 | FDSN 201** | Nutrition & Wellness | 3 | - | Humanities Elective | 4 | 3 |
| 4 | ITMO 340** | Introduction to Data Networks and the Internet | 3 | - | Minor Elective | 4 | 3 |
| - | - | - | - | CS 481 | Artificial Intelligence Language Understanding | 6 | 3 |
| 5 | ITMD 321** | Data Modeling and Applications | 3 | - | Minor Elective | 5 | 3 |
| 5 | MATH 574** | Bayesian Computational Statistics | 3 | - | Minor Elective | 6 | 3 |
| 5 | CAPSTONE 30X | Master Your Arena | 4 | - | IPRO Elective | 6 | 3 |
| TOTAL | 74 | 97 |
For 23 credits that aren't 1:1 mapped, we cover different courses worth 48 credits!