Machine learning course

• Course code: CSCI 5105
• Number of credits: 3
• Term: Fall 2022

Instructor's full name: Ph.D in mathematical modelling, Associate Professor Alex Avdyushenko, https://github.com/avalur

Sat 16:00-19:00

Telegram group for discussions and questions

Course duration: 45 classroom hours, 15 weeks: 3 hours a week

Course objectives: The course introduces the concepts and techniques in machine learning and deep learning. The course will first introduce basics of machine learning such as statistical learning models, linear and logistic regression, SVM, Bayes classifier and ensemble. The course will then introduce the basics of deep learning which include multi-layer neural networks, stochastic gradient descent, backpropagation, and optimization techniques. It will also cover topics which include recurrent neural networks, convolutional neural networks, generative networks, NLP and deep reinforcement learning. Coursework will consist of a few programming assignments and a final project in Python and PyTorch.

Knowledge:

• Understand basic concepts of machine learning and deep learning.
• Understand several types of machine and deep learning such as logistic regression, SVM, CNN, RNN, Attentions, GAN, DQN and their applications
• Acquire a working knowledge of implementing and training machine learning systems

Skills: Implement machine learning/deep learning applications using Python and PyTorch

Literature

Required: None (Hands-outs will be available)

Supplementary:

1. Pattern Recognition and Machine Learning, Christopher Bishop, 2006 https://goo.gl/EMbNKm
2. Introduction to Machine Learning, Ethem Alpaydin, 3rd Ed., 2014
3. Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2020. http://incompleteideas.net/book/RLbook2020.pdf
4. Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, https://goo.gl/4kVPrm
5. Neural Networks and Deep Learning, Michael A. Nielsen, 2015, http://neuralnetworksanddeeplearning.com/
6. Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, 2020
7. (in Russian) Deep Learning: Dive into The World of Neural Networks, S.I. Nikolenko, A.A. Kadurin, E.O. Arkhangelskaya, Piter, 2017, https://logic.pdmi.ras.ru/~sergey/books.html

Course calendar

10.09, Lecture 1. Introduction to Machine Learning

Course overview. Overview of machine learning and applications, K-NN. Python/Numpy Tutorial. Practice: K-NN

17.09, Lecture 2. Regression

Linear regression, Logistic regression and SGD. Practice: Linear/Logistic Regression

24.09, Lecture 3. SVM and Bias-Variance

Support Vector Machine (SVM), Bias-Variance. Practice: SVM

01.10, Lecture 4. Bayesian Learning and Ensemble

Naive Bayesian Classifier, Decision Tree, Ensemble. Practice: Bayesian Classifiers

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08.10, Lecture 5. Perceptron, MLP and Backpropagation

Perceptron, Softmax, Multi-Layer Perceptron (MLP), Multi-class Neural Networks, Backpropagation

Practice: Implementation of neural network with PyTorch for MNIST classification

Homework 1: SVM and Bayesian Classifiers

15.10, Lecture 6. CNN Basics

Convolutional Neural Nets (CNN), Pooling, ImageNet

Practice: Implementation of fully-connected NN with NumPy for House numbers classification

24.10, Lecture 7. Optimization and Training Techniques

Regularization, Optimization, Normalization, Dropout

Final Project Team Construct

Practice: Implementation of convolutional NN with PyTorch for CIFAR10

Homework 2: Image classification competition (Kaggle InClass)

29.10, Week 8: Midterm

05.11, Lecture 9: Recurrent Neural Networks (RNN)

Backpropagation through time (BPTT), RNN, Long short-term memory (LSTM), Gated recurrent unit (GRU), Encoder-Decoder architecture

Practice: Implementation RNN with PyTorch

12.11, Lecture 10: Attention, Transformers

Attention Networks, Bidirectional Encoder Representations from Transformers (BERT)

Practice: Attention, Transformers

19.11, Lecture 11: Autoencoder/VAE, GAN

Autoencoder, Variational AutoEncoder (VAE), Self-Supervised Learning, Generative Adversarial Net (GAN)

Practice: Autoencoder/VAE, GAN

Final Project Proposal: Team Idea presentation

26.11, Lecture 12: Intro to Reinforcement Learning (RL)

Reinforcement Learning, Q-Learning, Deep Q-Learning Network (DQN) and Policy Gradients

Practice: RL examples

03.12, Lecture 13: Final Project Presentation

10.12, Lecture 14: Final Project Presentation

17.12, Lecture 15: Final Project Presentation

Assessment criteria

#	Assessment criteria	Weeks																Total scores
		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1.	Midterm								8									30%
2.	Two Homeworks			3				7										20%
3.	Final Project							7						13	14			40%
5.	Class Participation	1	2	3	4	5	6	7	8	9	10	11	12					10%
	Total																	100%

Homework and Programming Assignments

Students are encouraged to discuss assignments with other students, both to share high-level understanding of the design or basic utilities, However, coding and writing of Homeworks must be done individually or by a team submitting as a group.

Final Project

Final project presentations schedule: link to table.

Instructor Alex A