Mata Kuliah ET4243 - 2024

Kode Mata KuliahET4243 / 3 SKS
Penyelenggara181 - Telecommunication Engineering / STEI
KategoriLecture
Bahasa IndonesiaEnglish
Nama Mata KuliahPembelajaran Mesin Lanjut untuk TelekomunikasiAdvance Machine Learning for Telecommunications
Bahan Kajian
  1. Simulasi dan Pemodelan
  2. Reinforcement Learning
  3. Deep Learning.
  4. Komunikasi Lisan
  5. Desain Rekayasa
  1. Simulation and Modeling
  2. Reinforcement Learning
  3. Deep Learning.
  4. Verbal Communication
  5. Engineering Design
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Kemampuan untuk menerapkan hubungan ilmiah dan matematis (prinsip atau hukum) dan masukan yang diperlukan untuk masalah yang diberikan pada simulasi dan pemodelan, reinforcement learning, dan deep learning.
  2. Kemampuan menganalisis masalah dan mengidentifikasi peluang untuk menghasilkan pernyataan masalah desain terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  3. Kemampuan mengidentifikasi batasan untuk menghasilkan persyaratan desain terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  4. Kemampuan mengidentifikasi dan merumuskan permasalahan teknik terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  5. Kemampuan menganalisis dan menyelesaikan permasalahan teknik terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  6. Kemampuan menerapkan penggunaan piranti teknik modern dan mengintegrasikan dalam proyek rekayasa terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  7. Kemampuan mempersiapkan dan mempresentasikan presentasi teknis secara lisan melalui berbagai media terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  8. Kemampuan mengumpulkan informasi tentang pengetahuan baru melalui media yang tersedia terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  9. Kemampuan memasukkan pengetahuan baru ke dalam pekerjaan teknik terkait simulasi dan pemodelan, reinforcement learning, dan deep learning.
  1. Ability to apply scientific and mathematical relationships (principles or laws) and necessary inputs to given problems in simulation and modeling, reinforcement learning, and deep learning.
  2. Ability to analyze problems and identify opportunities to produce design problem statements related to simulation and modeling, reinforcement learning, and deep learning.
  3. Ability to identify constraints to generate design requirements related to simulation and modeling, reinforcement learning, and deep learning.
  4. Ability to identify and formulate engineering problems related to simulation and modeling, reinforcement learning, and deep learning.
  5. Ability to analyze and solve engineering problems related to simulation and modeling, reinforcement learning, and deep learning.
  6. Ability to apply the use of modern engineering tools and integrate in engineering projects related to simulation and modeling, reinforcement learning, and deep learning.
  7. Ability to prepare and present technical presentations orally through various media related to simulation and modeling, reinforcement learning, and deep learning.
  8. Ability to collect information about new knowledge through available media related to simulation and modeling, reinforcement learning, and deep learning.
  9. Ability to incorporate new knowledge into engineering work related to simulation and modeling, reinforcement learning, and deep learning.
Metode PembelajaranCeramah Diskusi kelompok Pembelajaran berbasis Masalah/Studi KasusLecture Group discussion Problem/Case Study based learning
Modalitas PembelajaranLuring Sinkron Daring Asinkron BauranSynchronous Offline Asynchronous Online Mix
Jenis NilaiABCDE
Metode PenilaianKuis, UTS, UASQuizzes, Mid Semester Exam, Final Exam
Catatan TambahanDeskripsi kuliah: 1. Perkuliahan Offline a. Pengantar Machine Learning untuk Telekomunikasi, b. Tinjauan Traditional Learning, c. ANN (Reinforcement Learning),CNN (Recurrent Neural Network, dll), NLP; 2. Studi Kasus a. Ensemble Learning (prediksi coverage, peramalan trafik), b. Transformer / Generative AI (peramalan trafik, trajektori pengguna, prediksi coverage), c. Federated Learning (analisis trafik), d. Faster R-CNN (alokasi bandwidth), e. YOLO, deteksi objek bandwidth adaptif, f. Deep Learning (klasifikasi sinyal); 3. Proyek a. ML, non-traditional learning pada telekomunikasiCourse description: 1. Off line courses : a. Introduction for macchine learning in telecommunication, b. Review of traditional learning, c. ANN (Reinforcement learning), CNN (Recurrent Network,etc), NLP; 2. Case studies : a. Ensamble Learning (Coverage prediction, traffic forecast), b. Transformer/Generative AI (Traffic forecast, User Trajectory, Coverage Prediction), c. Federated Learning (Traffic Analysis), d. Faster R-CNN (Bandwidth Allocator), e. YOLO, Adaptive Bandwidth Object Detection, f. Deep learning (Signal classification); 3. Project : a. ML - non traditional learning for telecommunication project