2019 Machine Learning

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Academic unit or major
Graduate major in Artificial Intelligence
Okazaki Naoaki  Shimosaka Masamichi 
Course component(s)
Day/Period(Room No.)
Tue3-4(H101,J232)  Fri3-4(H101,J232)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

This course introduces basic knowledge of machine learning and deep learning.

Student learning outcomes

- 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

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- - - -

Class flow

This lecture includes explanations and exercises of machine learning toolkits.

Course schedule/Required learning

  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, mini-batch, distributed parallel training
Class 8 Introduction to Deep Learning Real-world applications
Class 9 Deep Feedforward Networks multi-layer perceptron, hidden units, activation functions, computational graph
Class 10 Training Deep Models gradient-based learning, back propagation, regularization, dropout
Class 11 Representation Learning distributed representation, word embeddings, latent semantic analysis, skip-gram 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 short-term memory, gated recurrent unit
Class 14 Encoder Decoder Modeling sequence-to-sequence models, attention mechanisms
Class 15 Applications of Deep Learning text generation, implementation


Handouts will be given when necessary.

Reference books, course materials, etc.

- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. 2016.
- Christopher Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics), 2010

Assessment criteria and methods

Course marks are based on assignments (70%) and exercises (30%).

Related courses

  • MCS.T507 : Theory of Statistical Mathematics
  • MCS.T403 : Statistical Learning Theory
  • CSC.T352 : Pattern Recognition
  • CSC.T272 : Artificial Intelligence
  • CSC.T242 : Probability Theory and Statistics

Prerequisites (i.e., required knowledge, skills, courses, etc.)




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