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study.json

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"resources": []
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}
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]
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},
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{
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"id": "sem4",
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"title": "Semester 4",
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"tag": "S4",
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"icon": "fa-solid fa-4",
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"accent": "purple",
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"blurb": "Data, Signals & First Steps into ML",
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"children": [
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{
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"id": "prob-stats",
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"title": "Probability and Statistics Theory",
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"tag": "MATH",
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"icon": "fa-solid fa-chart-simple",
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"accent": "blue",
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"blurb": "Distributions, inference, estimation",
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"resources": []
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},
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{
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"id": "dsa",
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"title": "Data Structures and Algorithms",
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"tag": "CS",
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"icon": "fa-solid fa-sitemap",
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"accent": "green",
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"blurb": "Trees, graphs, complexity, core algorithms",
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"resources": []
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},
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{
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"id": "python-programming",
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"title": "Python Programming",
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"tag": "CS",
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"icon": "fa-brands fa-python",
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"accent": "teal",
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"blurb": "Applied Python beyond the basics",
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"resources": []
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},
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{
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"id": "dsp",
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"title": "Digital Signals Processing",
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"tag": "SIG",
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"icon": "fa-solid fa-wave-square",
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"accent": "orange",
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"blurb": "Sampling, filters, transforms",
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"resources": []
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},
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{
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"id": "dsp-lab",
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"title": "Digital Signals Processing Lab",
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"tag": "LAB",
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"icon": "fa-solid fa-flask-vial",
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"accent": "amber",
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"blurb": "Hands-on DSP implementation",
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"resources": []
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},
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{
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"id": "intro-ml",
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"title": "Ideal Machine Learning",
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"tag": "ML",
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"icon": "fa-solid fa-brain",
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"accent": "red",
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"blurb": "First formal introduction to ML",
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"resources": []
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}
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{
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"id": "sem5",
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"title": "Semester 5",
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"tag": "S5",
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"icon": "fa-solid fa-5",
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"accent": "teal",
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"blurb": "Intelligence, Language & Architecture",
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"children": [
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{
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"id": "comp-arch",
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"title": "Computer Architecture",
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"tag": "SYS",
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"icon": "fa-solid fa-server",
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"accent": "blue",
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"blurb": "CPU design, memory hierarchy, pipelines",
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"resources": []
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},
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{
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"id": "ai",
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"title": "AI",
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"tag": "AI",
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"icon": "fa-solid fa-microchip",
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"accent": "red",
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"blurb": "Search, reasoning, knowledge representation",
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"resources": []
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},
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{
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"id": "nlp",
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"title": "NLP",
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"tag": "NLP",
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"icon": "fa-solid fa-comment-dots",
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"accent": "purple",
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"blurb": "Text processing, language models",
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"resources": []
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{
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"id": "info-retrieval",
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"title": "Information Retrieval",
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"tag": "IR",
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"accent": "amber",
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"blurb": "Search engines, ranking, indexing",
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"resources": []
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{
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"id": "chatbot-dev",
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"title": "ChatBot Development",
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"tag": "APP",
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"icon": "fa-solid fa-robot",
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"accent": "green",
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"blurb": "Conversational AI systems, applied build",
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"resources": []
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}
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]
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},
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{
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"id": "sem6",
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"title": "Semester 6",
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"tag": "S6",
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"icon": "fa-solid fa-6",
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"accent": "orange",
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"blurb": "Low-Level Systems to Deep Learning",
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"children": [
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{
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"id": "assembly",
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"title": "Assembly Language",
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"tag": "SYS",
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"icon": "fa-solid fa-terminal",
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"blurb": "Low-level programming & instruction sets",
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{
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"id": "data-analysis",
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"title": "Data Analysis",
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"blurb": "EDA, cleaning, statistical analysis",
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"resources": []
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{
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"id": "ml",
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"title": "Machine Learning",
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"blurb": "Core ML algorithms & theory",
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{
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"id": "dl",
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"title": "Deep Learning",
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"blurb": "Neural networks, backprop, architectures",
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{
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"id": "opt-spec-1",
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"title": "OPT SPEC 1",
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"tag": "ELECTIVE",
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"icon": "fa-solid fa-star",
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"accent": "amber",
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"blurb": "Optional specialization track — theory",
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"resources": []
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{
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"id": "opt-spec-1-practical",
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"title": "OPT SPEC 1 (PRACTICAL)",
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"tag": "ELECTIVE",
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"blurb": "Optional specialization track — hands-on",
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]
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{
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"id": "sem7",
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"title": "Semester 7",
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"tag": "S7",
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"icon": "fa-solid fa-7",
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"accent": "red",
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"blurb": "Deepening Specialization & Research",
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{
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"id": "opt-spec-2",
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"blurb": "Optional specialization track — theory",
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{
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"id": "opt-spec-3",
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"blurb": "Optional specialization track — theory",
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{
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"id": "opt-spec-3-practical",
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{
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"title": "OPT SPEC 4",
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"icon": "fa-solid fa-meteor",
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"blurb": "Optional specialization track — theory",
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{
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"id": "research-paper",
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"accent": "green",
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"blurb": "Final Specialization & Graduation",
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"title": "OPT SPEC 5",
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"blurb": "Optional specialization track — theory",
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"id": "opt-spec-5-practical",
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"title": "OPT SPEC 5 (PRACTICAL)",
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"blurb": "Optional specialization track — hands-on",
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"resources": []
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{
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"id": "graduation-project",
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"title": "Graduation Project",
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"tag": "CAPSTONE",
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"accent": "red",
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"blurb": "",
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]
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}
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]
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},

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