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Practical Machine Learning

Dr. Suyong Eum / Dr. Hua Yang

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Lectures / Tutorials

General information.

test Lecture room: C401
test Friday: 10:30 ~ 12:00

Lecture schedule

The following table provides a brief summary of the tentative topics and a link to a PowerPoint file in pdf format containing the notes. Where errors occur in the notes, updates will be posted as soon as practicable. The notes will be made available prior to lectures where-ever practicable. You will need to use Acrobat Reader to obtain this material as it will be in pdf format.

Week
Topic
Date
Lecturer
W1
Introduction to Machine Learning
APR. 13th
Dr. EUM
W2
Linear models for classification and regression
APR. 20th
Dr. EUM
W3
K-means model and Gaussian Mixture Model (GMM)
APR. 27th
Dr. EUM
W4
Hidden Markov Model (HMM)
MAY 11th
Dr. EUM
W5
Support Vector Machine (SVM) and Kernel trick
MAY 18th
Dr. EUM
W6
Principal Component Analysis (PCA)
MAY 25th
Dr. EUM
W7
Neural Networks
JUN. 1st
Dr. EUM
W8
Convolutional Neural Networks (CNN)
JUN. 8th
Dr. EUM
W9
Tensorflow - CNN implementation
JUN. 15th
Dr. EUM
W10
Recurrent Neural Networkss (RNN) and Long Short Term Memory (LSTM)
JUN. 22nd
Dr. EUM
W11
Tensorflow - RNN / LSTM / GRU implementation
JUN. 29th
Dr. EUM
W12
Generative Models: Variational Auto Encoder (VAE) and Generative Adversarial Network (GAN)
JUL. 6th
Dr. EUM
W13
Tensorflow - VAE / DCGAN implemenation
JUL. 13th
Dr. EUM
W14
Reinforcement Learning (RL):Deep Q Networks (DQN) and Policy Gradient(PG: AC/A3C)
JUL. 20th
Dr. EUM
W15
Tensorflow - DQN / PG / AC implementation
JUL. 27th
Dr. EUM

Tutorial schedule

There will be four tutorial sessions during this semester. The completion of the tutorials will greatly help you to carry out the first assignment. Thus, your participation is highly encouraged. The date and time will be announced in the lecture.

Tutorial sessions will be carried out on the day as shown below from 11:00 to 12:00 at C401.

Week
Topic
Code
Date
Tutor
W2
Introduction to Python
APR. 20th
Dr. YANG
W3
Perceptron algorithm with scikit-learn
APR. 27th
Dr. YANG
W6
Support Vector Machine (SVM) with scikit-learn
MAY. 25th
Dr. YANG
W7
Principal Component Analysis (PCA) with scikit-learn
JUN. 1st
Dr. YANG
W10
Tensorflow: Deep Neural Networks(DNN): PART I - Model definition
JUN. 20th
Dr. EUM
W11
Tensorflow: Deep Neural Networks(DNN): PART II - Evaluation
JUN. 27th
Dr. EUM
W12
Tensorflow: Convolutional Neural Networks(CNN): data loading - Queuerunner
JUL. 4th
Dr. EUM

References

1. Christopher M. Bishop “Pattern Recognition and Machine Learning,” Springer, 2011. ISBN-10: 0387310730. (online downloadable)

2. Tom M. Mitchell “Machine Learning,” March, 1997. ISBN: 0070428077. (online downloadable)

 


Copyright © 2011 OSAKA University - Last update: January 20, 2019, 8:38 pm by Suyong Eum.

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