|
206 | 206 | "resources": [] |
207 | 207 | } |
208 | 208 | ] |
| 209 | + }, |
| 210 | + { |
| 211 | + "id": "sem4", |
| 212 | + "title": "Semester 4", |
| 213 | + "tag": "S4", |
| 214 | + "icon": "fa-solid fa-4", |
| 215 | + "accent": "purple", |
| 216 | + "blurb": "Data, Signals & First Steps into ML", |
| 217 | + "children": [ |
| 218 | + { |
| 219 | + "id": "prob-stats", |
| 220 | + "title": "Probability and Statistics Theory", |
| 221 | + "tag": "MATH", |
| 222 | + "icon": "fa-solid fa-chart-simple", |
| 223 | + "accent": "blue", |
| 224 | + "blurb": "Distributions, inference, estimation", |
| 225 | + "resources": [] |
| 226 | + }, |
| 227 | + { |
| 228 | + "id": "dsa", |
| 229 | + "title": "Data Structures and Algorithms", |
| 230 | + "tag": "CS", |
| 231 | + "icon": "fa-solid fa-sitemap", |
| 232 | + "accent": "green", |
| 233 | + "blurb": "Trees, graphs, complexity, core algorithms", |
| 234 | + "resources": [] |
| 235 | + }, |
| 236 | + { |
| 237 | + "id": "python-programming", |
| 238 | + "title": "Python Programming", |
| 239 | + "tag": "CS", |
| 240 | + "icon": "fa-brands fa-python", |
| 241 | + "accent": "teal", |
| 242 | + "blurb": "Applied Python beyond the basics", |
| 243 | + "resources": [] |
| 244 | + }, |
| 245 | + { |
| 246 | + "id": "dsp", |
| 247 | + "title": "Digital Signals Processing", |
| 248 | + "tag": "SIG", |
| 249 | + "icon": "fa-solid fa-wave-square", |
| 250 | + "accent": "orange", |
| 251 | + "blurb": "Sampling, filters, transforms", |
| 252 | + "resources": [] |
| 253 | + }, |
| 254 | + { |
| 255 | + "id": "dsp-lab", |
| 256 | + "title": "Digital Signals Processing Lab", |
| 257 | + "tag": "LAB", |
| 258 | + "icon": "fa-solid fa-flask-vial", |
| 259 | + "accent": "amber", |
| 260 | + "blurb": "Hands-on DSP implementation", |
| 261 | + "resources": [] |
| 262 | + }, |
| 263 | + { |
| 264 | + "id": "intro-ml", |
| 265 | + "title": "Ideal Machine Learning", |
| 266 | + "tag": "ML", |
| 267 | + "icon": "fa-solid fa-brain", |
| 268 | + "accent": "red", |
| 269 | + "blurb": "First formal introduction to ML", |
| 270 | + "resources": [] |
| 271 | + } |
| 272 | + ] |
| 273 | + }, |
| 274 | + { |
| 275 | + "id": "sem5", |
| 276 | + "title": "Semester 5", |
| 277 | + "tag": "S5", |
| 278 | + "icon": "fa-solid fa-5", |
| 279 | + "accent": "teal", |
| 280 | + "blurb": "Intelligence, Language & Architecture", |
| 281 | + "children": [ |
| 282 | + { |
| 283 | + "id": "comp-arch", |
| 284 | + "title": "Computer Architecture", |
| 285 | + "tag": "SYS", |
| 286 | + "icon": "fa-solid fa-server", |
| 287 | + "accent": "blue", |
| 288 | + "blurb": "CPU design, memory hierarchy, pipelines", |
| 289 | + "resources": [] |
| 290 | + }, |
| 291 | + { |
| 292 | + "id": "ai", |
| 293 | + "title": "AI", |
| 294 | + "tag": "AI", |
| 295 | + "icon": "fa-solid fa-microchip", |
| 296 | + "accent": "red", |
| 297 | + "blurb": "Search, reasoning, knowledge representation", |
| 298 | + "resources": [] |
| 299 | + }, |
| 300 | + { |
| 301 | + "id": "nlp", |
| 302 | + "title": "NLP", |
| 303 | + "tag": "NLP", |
| 304 | + "icon": "fa-solid fa-comment-dots", |
| 305 | + "accent": "purple", |
| 306 | + "blurb": "Text processing, language models", |
| 307 | + "resources": [] |
| 308 | + }, |
| 309 | + { |
| 310 | + "id": "info-retrieval", |
| 311 | + "title": "Information Retrieval", |
| 312 | + "tag": "IR", |
| 313 | + "icon": "fa-solid fa-magnifying-glass", |
| 314 | + "accent": "amber", |
| 315 | + "blurb": "Search engines, ranking, indexing", |
| 316 | + "resources": [] |
| 317 | + }, |
| 318 | + { |
| 319 | + "id": "chatbot-dev", |
| 320 | + "title": "ChatBot Development", |
| 321 | + "tag": "APP", |
| 322 | + "icon": "fa-solid fa-robot", |
| 323 | + "accent": "green", |
| 324 | + "blurb": "Conversational AI systems, applied build", |
| 325 | + "resources": [] |
| 326 | + } |
| 327 | + ] |
| 328 | + }, |
| 329 | + { |
| 330 | + "id": "sem6", |
| 331 | + "title": "Semester 6", |
| 332 | + "tag": "S6", |
| 333 | + "icon": "fa-solid fa-6", |
| 334 | + "accent": "orange", |
| 335 | + "blurb": "Low-Level Systems to Deep Learning", |
| 336 | + "children": [ |
| 337 | + { |
| 338 | + "id": "assembly", |
| 339 | + "title": "Assembly Language", |
| 340 | + "tag": "SYS", |
| 341 | + "icon": "fa-solid fa-terminal", |
| 342 | + "accent": "blue", |
| 343 | + "blurb": "Low-level programming & instruction sets", |
| 344 | + "resources": [] |
| 345 | + }, |
| 346 | + { |
| 347 | + "id": "data-analysis", |
| 348 | + "title": "Data Analysis", |
| 349 | + "tag": "DATA", |
| 350 | + "icon": "fa-solid fa-chart-line", |
| 351 | + "accent": "teal", |
| 352 | + "blurb": "EDA, cleaning, statistical analysis", |
| 353 | + "resources": [] |
| 354 | + }, |
| 355 | + { |
| 356 | + "id": "ml", |
| 357 | + "title": "Machine Learning", |
| 358 | + "tag": "ML", |
| 359 | + "icon": "fa-solid fa-brain", |
| 360 | + "accent": "red", |
| 361 | + "blurb": "Core ML algorithms & theory", |
| 362 | + "resources": [] |
| 363 | + }, |
| 364 | + { |
| 365 | + "id": "dl", |
| 366 | + "title": "Deep Learning", |
| 367 | + "tag": "DL", |
| 368 | + "icon": "fa-solid fa-network-wired", |
| 369 | + "accent": "purple", |
| 370 | + "blurb": "Neural networks, backprop, architectures", |
| 371 | + "resources": [] |
| 372 | + }, |
| 373 | + { |
| 374 | + "id": "opt-spec-1", |
| 375 | + "title": "OPT SPEC 1", |
| 376 | + "tag": "ELECTIVE", |
| 377 | + "icon": "fa-solid fa-star", |
| 378 | + "accent": "amber", |
| 379 | + "blurb": "Optional specialization track — theory", |
| 380 | + "resources": [] |
| 381 | + }, |
| 382 | + { |
| 383 | + "id": "opt-spec-1-practical", |
| 384 | + "title": "OPT SPEC 1 (PRACTICAL)", |
| 385 | + "tag": "ELECTIVE", |
| 386 | + "icon": "fa-solid fa-screwdriver-wrench", |
| 387 | + "accent": "green", |
| 388 | + "blurb": "Optional specialization track — hands-on", |
| 389 | + "resources": [] |
| 390 | + } |
| 391 | + ] |
| 392 | + }, |
| 393 | + { |
| 394 | + "id": "sem7", |
| 395 | + "title": "Semester 7", |
| 396 | + "tag": "S7", |
| 397 | + "icon": "fa-solid fa-7", |
| 398 | + "accent": "red", |
| 399 | + "blurb": "Deepening Specialization & Research", |
| 400 | + "children": [ |
| 401 | + { |
| 402 | + "id": "opt-spec-2", |
| 403 | + "title": "OPT SPEC 2", |
| 404 | + "tag": "ELECTIVE", |
| 405 | + "icon": "fa-solid fa-star", |
| 406 | + "accent": "amber", |
| 407 | + "blurb": "Optional specialization track — theory", |
| 408 | + "resources": [] |
| 409 | + }, |
| 410 | + { |
| 411 | + "id": "opt-spec-2-practical", |
| 412 | + "title": "OPT SPEC 2 (PRACTICAL)", |
| 413 | + "tag": "ELECTIVE", |
| 414 | + "icon": "fa-solid fa-screwdriver-wrench", |
| 415 | + "accent": "green", |
| 416 | + "blurb": "Optional specialization track — hands-on", |
| 417 | + "resources": [] |
| 418 | + }, |
| 419 | + { |
| 420 | + "id": "opt-spec-3", |
| 421 | + "title": "OPT SPEC 3", |
| 422 | + "tag": "ELECTIVE", |
| 423 | + "icon": "fa-solid fa-star-half-stroke", |
| 424 | + "accent": "purple", |
| 425 | + "blurb": "Optional specialization track — theory", |
| 426 | + "resources": [] |
| 427 | + }, |
| 428 | + { |
| 429 | + "id": "opt-spec-3-practical", |
| 430 | + "title": "OPT SPEC 3 (PRACTICAL)", |
| 431 | + "tag": "ELECTIVE", |
| 432 | + "icon": "fa-solid fa-gears", |
| 433 | + "accent": "teal", |
| 434 | + "blurb": "Optional specialization track — hands-on", |
| 435 | + "resources": [] |
| 436 | + }, |
| 437 | + { |
| 438 | + "id": "opt-spec-4", |
| 439 | + "title": "OPT SPEC 4", |
| 440 | + "tag": "ELECTIVE", |
| 441 | + "icon": "fa-solid fa-meteor", |
| 442 | + "accent": "orange", |
| 443 | + "blurb": "Optional specialization track — theory", |
| 444 | + "resources": [] |
| 445 | + }, |
| 446 | + { |
| 447 | + "id": "research-paper", |
| 448 | + "title": "Research Paper", |
| 449 | + "tag": "RESEARCH", |
| 450 | + "icon": "fa-solid fa-file-lines", |
| 451 | + "accent": "blue", |
| 452 | + "blurb": "Independent research & academic writing", |
| 453 | + "resources": [] |
| 454 | + } |
| 455 | + ] |
| 456 | + }, |
| 457 | + { |
| 458 | + "id": "sem8", |
| 459 | + "title": "Semester 8", |
| 460 | + "tag": "S8", |
| 461 | + "icon": "fa-solid fa-8", |
| 462 | + "accent": "green", |
| 463 | + "blurb": "Final Specialization & Graduation", |
| 464 | + "children": [ |
| 465 | + { |
| 466 | + "id": "opt-spec-5", |
| 467 | + "title": "OPT SPEC 5", |
| 468 | + "tag": "ELECTIVE", |
| 469 | + "icon": "fa-solid fa-crown", |
| 470 | + "accent": "amber", |
| 471 | + "blurb": "Optional specialization track — theory", |
| 472 | + "resources": [] |
| 473 | + }, |
| 474 | + { |
| 475 | + "id": "opt-spec-5-practical", |
| 476 | + "title": "OPT SPEC 5 (PRACTICAL)", |
| 477 | + "tag": "ELECTIVE", |
| 478 | + "icon": "fa-solid fa-toolbox", |
| 479 | + "accent": "teal", |
| 480 | + "blurb": "Optional specialization track — hands-on", |
| 481 | + "resources": [] |
| 482 | + }, |
| 483 | + { |
| 484 | + "id": "graduation-project", |
| 485 | + "title": "Graduation Project", |
| 486 | + "tag": "CAPSTONE", |
| 487 | + "icon": "fa-solid fa-trophy", |
| 488 | + "accent": "red", |
| 489 | + "blurb": "", |
| 490 | + "resources": [] |
| 491 | + } |
| 492 | + ] |
209 | 493 | } |
210 | 494 | ] |
211 | 495 | }, |
|
0 commit comments