> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/course/mk-machine-learning.md).

# 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)