This course introduces basic knowledge of machine learning and deep learning.
[Goal]
 Understand basic concepts (e.g., classification, convex optimization) and methods (e.g., stochastic gradient descent, back propagation) for discriminative models of machine learning.
 Realize machine learning with toolkits and programming.
[Theme] The first half of this lecture covers basic concept of machine learning with linear models and optimization. The second half of this lecture presents the fundamentals and practices of deep learning.
Machine learning, regression, classification, optimization, linear model, neural network, deep learning
Intercultural skills  Communication skills  Specialist skills  Critical thinking skills  Practical and/or problemsolving skills 

    ✔     
This lecture includes explanations and exercises of machine learning toolkits.
Course schedule  Required learning  

Class 1  introduction  Basic concept of Machine Learning 
Class 2  Linear Model 1  Loss functions, empirical loss minimization, overfitting, regularization, bias and variance, linear model (linear regression) 
Class 3  Optimization 1  Concept of optimization, gradient methods, constraint optimization. 
Class 4  Optimization 2  Convex optimization, Duality 
Class 5  Linear Model 2  Linear model (classification)，logistic regression, linear and kernel support vector machines 
Class 6  Linear Model 3  L1 regularization, sparse learning, Lasso 
Class 7  Scalable Learning  Stochastic gradient, accelerated gradients, moment, minibatch, distributed parallel training 
Class 8  Introduction to Deep Learning  Realworld applications 
Class 9  Deep Feedforward Networks  multilayer perceptron, hidden units, activation functions, computational graph 
Class 10  Training Deep Models  gradientbased learning, back propagation, regularization, dropout 
Class 11  Representation Learning  distributed representation, word embeddings, latent semantic analysis, skipgram model 
Class 12  Convolutional Networks  convolution, pooling, gated linear unit, feature extraction 
Class 13  Recurrent and Recursive Nets  recurrent neural networks, recursive neural networks, long shortterm memory, gated recurrent unit 
Class 14  Encoder Decoder Modeling  sequencetosequence models, attention mechanisms 
Class 15  Applications of Deep Learning  text generation, implementation 
Handouts will be given when necessary.
 Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. 2016.
 Christopher Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics), 2010

Course marks are based on assignments (70%) and exercises (30%).
None
None.