# MK Machine Learning * Kode: TKE194918 * SKS: 3 * Jadwal * TKE194918 Machine Learning A RABU 15:00 - 17:30 GEDUNG TEKNIK E 201 - 12 mhs ## Sumber Referensi * Materi [Kuliah dari Andrew Ng](https://irosyadi.netlify.app/course/machine-learning-andrewng/) * [Python Data Science](https://github.com/leriomaggio/python-data-science) * [Python ML Course](https://github.com/leriomaggio/python-ml-course) * [TensorFlow, Keras and deep learning, without a PhD](https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist#0), [Github](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd) * [CS231n Github](https://cs231n.github.io/), [CS231n Github Source](https://github.com/cs231n/cs231n.github.io) * [Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning) in Python License: MIT * [Machine Learning Octave](https://github.com/trekhleb/machine-learning-octave) in Octave License: MIT * [Machine Learning Experiments](https://github.com/trekhleb/machine-learning-experiments) License: MIT * [COMS W4995 Applied Machine Learning Spring 2019 - Schedule - Andreas C. Müller - Associate Research Scientist](https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/), [amueller/COMS4995-s19: COMS W4995 Applied Machine Learning - Spring 19](https://github.com/amueller/COMS4995-s19) License: CC0 ## Tools * [Colabify](https://chrome.google.com/webstore/detail/github-colabify/enfgannencjofjonlojjahlblnjnfhon/related?hl=en) * [Notebook Buddy](https://chrome.google.com/webstore/detail/notebook-buddy/kmhoiofjdpbiodaggadcibdkicfgplcl) ## Kuliah ### Pekan 7-9 * Logistic regression - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/pdf/Lecture6.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/ppt/Lecture6.pptx) * Regularization - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/pdf/Lecture7.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/ppt/Lecture7.pptx) * Programming Exercise 2: Logistic Regression - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/exercises/machine-learning-ex2/ex2.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/exercises/machine-learning-ex2.zip) - [Solution](https://vkosuri.github.io/home/week-3/exercises/machine-learning-ex2/ex2) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/errata.pdf) * [06: Logistic Regression by Holehouse](https://www.holehouse.org/mlclass/06_Logistic_Regression.html) * [07: Regularization by Holehouse](https://www.holehouse.org/mlclass/07_Regularization.html) ### Pekan 10-12 * Neural Networks: Representation - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/lectures/pdf/Lecture8.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/lectures/ppt/Lecture8.pptx) * Programming Exercise 3: Multi-class Classification and Neural Networks - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/exercises/machine-learning-ex3/ex3.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/exercises/machine-learning-ex3.zip) - [Solution](https://vkosuri.github.io/home/week-4/exercises/machine-learning-ex3/ex3) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-4/exercises/Programming%20Ex.3.pdf) * [08: Neural Networks - Representation by Holehouse](https://www.holehouse.org/mlclass/08_Neural_Networks_Representation.html) ### Pekan 13 * Neural Networks: Learning - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/lectures/pdf/Lecture9.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/lectures/ppt/Lecture9.pptx) * Programming Exercise 4: Neural Networks Learning - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/exercises/machine-learning-ex4/ex4.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/exercises/machine-learning-ex4.zip) - [Solution](https://vkosuri.github.io/home/week-5/exercises/machine-learning-ex4/ex4) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-4/exercises/Programming%20Ex.4.pdf) * [09: Neural Networks - Learning by Holehouse](https://www.holehouse.org/mlclass/09_Neural_Networks_Learning.html) ### Visualizing Backpropagation * [HMKCode](https://hmkcode.com/ai/backpropagation-step-by-step/) * [MattMazur](https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/) * [JeremyJordan](https://www.jeremyjordan.me/neural-networks-training/) * [NN](https://github.com/adityamarella/neuralnetwork/blob/master/NN.m) ### Pekan 14 : Deep Learning Introduction * [TensorFlow, Keras and deep learning, without a PhD](https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist#0), [Github](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://irosyadi.gitbook.io/irosyadi/course/mk-machine-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.