Curriculum Library — Open Course Materials

Skill outcomes

• Apply linear algebra, probability, and statistics to machine learning problems
• Implement classical ML algorithms in scikit-learn end-to-end
• Evaluate models using appropriate metrics and the bias-variance tradeoff
• Run principled model selection and hyperparameter tuning workflows

Skill outcomes

• Build CNNs, RNNs, and transformer models in PyTorch 2.x
• Train networks at scale with regularization and modern optimization
• Apply transfer learning to adapt pretrained models to new tasks
• Operate GPU training workflows in the on-campus Kompas AI Studio

Prerequisites: A1

Skill outcomes

• Derive transformer architectures from tokenization and attention first principles
• Fine-tune small open-weight models such as Llama- and Mistral-class LLMs
• Distinguish pretraining, instruction tuning, and RLHF in practice
• Design evaluation suites for NLP and LLM tasks

Prerequisites: A2

Skill outcomes

• Train and condition diffusion models including Stable Diffusion and ControlNet
• Reason about diffusion math intuition across image, audio, and video modalities
• Evaluate generative output with task-appropriate quality and safety metrics
• Implement watermarking and provenance for generated content

Prerequisites: A2

Skill outcomes

• Build deployable agentic applications using LangChain, LangGraph, and MCP
• Architect RAG pipelines over vector databases like pgvector, Qdrant, and Weaviate
• Instrument agent observability, evaluation, and cost management
• Apply orchestration patterns for multi-step tool use in production

Prerequisites: A3

Skill outcomes

• Serve models in production with FastAPI, BentoML, and Kubernetes
• Deploy workloads on AWS Bedrock/SageMaker, Azure AI Foundry, and GCP Vertex AI
• Run prompt management, A/B testing, and gradual rollouts for AI features
• Operate observability stacks tailored to ML and LLM services

Prerequisites: A5

Skill outcomes

• Assess models for bias and fairness across deployment contexts
• Run red-teaming exercises against LLM and agentic systems
• Map products to the EU AI Act, India's DPDP Act, and sectoral compliance
• Publish model cards and system cards documenting alignment posture

Prerequisites: A5

Skill outcomes

• Scope and design an AI product with a sponsoring industry partner
• Build and deploy a working AI service used by the sponsoring company
• Document the system with a model card, system card, and runbook
• Defend the product in co-evaluation by university faculty and industry sponsor

Prerequisites: A6 → A7

Skill outcomes

• Run Porter-meets-AI competitive analysis on a real industry
• Conduct LLM-driven scenario planning and market intelligence sweeps
• Apply build-vs-buy frameworks to AI capability decisions
• Diagnose organizations against an AI maturity model

Skill outcomes

• Build AI-powered marketing campaigns end-to-end
• Design ML-driven customer segmentation and personalization engines
• Operate AI creative pipelines for copy, image, and video
• Measure AI marketing ROI within privacy and consent constraints

Prerequisites: C1

Skill outcomes

• Lead discovery and prioritization for AI products under uncertainty
• Prototype AI products using Cursor, Lovable, and v0
• Design evaluation strategies for non-deterministic systems
• Plan GTM and pricing for AI products

Prerequisites: C1

Skill outcomes

• Apply ML to credit scoring, fraud detection, and algorithmic trading basics
• Use GenAI for equity research and BFSI operations workflows
• Navigate RBI guidelines and the DPDP Act for financial AI
• Run AI risk management for financial services use cases

Prerequisites: C1

Skill outcomes

• Build demand forecasting and route optimization models
• Deploy predictive maintenance and warehouse AI workflows
• Apply AI to retail and logistics operations
• Conduct a data-readiness audit for an operations function

Prerequisites: C1

Skill outcomes

• Implement AI-assisted sourcing, screening, and attrition modeling
• Run sentiment analysis on internal communications
• Design ethical guardrails for AI in hiring
• Map the legal exposure landscape for HR AI deployments

Prerequisites: C1

Skill outcomes

• Apply EU AI Act, India DPDP, and sectoral compliance frameworks
• Evaluate AI vendors and AI investments rigorously
• Use ethical decision frameworks to triage AI deployments
• Lead a board-level conversation on AI investment without external consultants

Prerequisites: C1 → C2 → C3 → C4 → C5 → C6

Skill outcomes

• Deliver a semester-long consulting engagement for a partner company
• Redesign an AI-driven go-to-market or AI-readiness audit for a real client
• Develop a deployable AI governance framework for a BFSI or mid-market sponsor
• Defend recommendations to a joint university and industry evaluation panel

Prerequisites: C1 → C2 → C3 → C4 → C5 → C6 → C7

Skill outcomes

• Train your faculty cohort across every department to integrate AI into teaching, research, and administrative work
• Equip lecturers with hands-on Python and PyTorch fluency through Tier 3 deep technical upskilling
• Build discipline-specific AI fluency for ten departments via Tier 2 tracks
• Produce internal Kompas-trained co-faculty who can lead Tracks A–D delivery by year three

Skill outcomes

• Stand up a publishable AI research group within 18 months, with embedded PhD-level mentors and GPU credits
• Mentor faculty and PhD students through paper submission to NeurIPS workshops, ICLR, IndoML, and discipline journals
• Win first research grant within 24 months across DST, MeitY, AICTE, or ICMR funding lines
• Drive first NIRF research-score uplift within 36 months through industry research partnerships and joint publications

Skill outcomes

• Map your AI curriculum to the NEP 2020 credit framework across specializations, minors, AECCs, VACs, and SECs
• Generate NAAC, NBA, and NIRF evidence packs covering labs, faculty, outcomes, and published research
• Align courses with the AICTE Model Curriculum and complete OBE mapping with PO/CO matrices
• Get a named Kompas accreditation lead embedded for the reaccreditation cycle

Skill outcomes

• Stand up an AI startup incubator on campus with Kompas-trained Entrepreneurs-in-Residence embedded full-time
• Run a pre-incubation idea-to-MVP program for student teams, with 12 months of cloud and tooling credits covered
• Bridge student capstones to angel and seed funding via Kompas's investor and acquirer network
• Align student, university, and Kompas incentives through a shared equity carry on incubated companies that exit