pdf slides Lecture: Thursday, Jan 12 | | Review of probability Fundamentals of machine learning pdf slides |
Tutorial | | Review of linear algebra Introduction to TensorFlow pdf slides Tutorial examples as an IPython Notebook. Take a look at here on how to install Jupyter/IPython Notebook. |
Week 2 | | |
Lecture: Monday, Jan 16 | The curse of dimensionality: Bishop 2006, Chap. 1.4 K-NN: Bishop 2006, Chap. 2.5.2 (free) K-NN and linear regression: Hastie et al 2013, Chap. 2.3 (free) Convex function and Jensen inequality: MacKay 2003, Chap. 2.7 (free) Gradient descent: Goodfellow et al 2016, Chap. 4.3 | Example: K Nearest Neighbours Optimization pdf slides |
Lecture: Thursday, Jan 19 | Stochastic gradient descent, Léon Bottou The momentum method: Coursera video: Neural Networks for Machine Learning Lecture 6.3 Maximum likelihood for a Gaussian: MacKay 2003, Chap. 22.1 Maximum likelihhod estimation of a classifier: Hastie et al 2013, Chap. 2.6.3 Regularization: Goodfellow et al 2016, Chap. 7.1 Regularization through data augmentation: Goodfellow et al 2016, Chap. 7.4 | Maximum likelihood estimation (MLE)] Optimization and regularization pdf slides |
Tutorial | | Tricks to improve SGD “Tuning/debugging” optimizer Multivariate Gaussian Underfitting vs. overfitting pdf slides |
Week 3 | | |
Lecture: Monday, Jan 23 | | Probabilistic interpretation of linear regression MLE vs. MAP Optimal regressor [pdf slides] (see the joint slide deck from Jan 26) |
Assignment 1: Wednesday, Jan 25 | Due date: Feb 7 midnight, 2017 k-NN, Gaussian process (bonus), linear regression | Assignment handout Download Tiny MNIST dataset here Histogram of results |
Lecture: Thursday, Jan 26 | Regression and decision theory: Bishop 2006, Chap. 1.5 Bias-variance trade-off: Bishop 2006, Chap. 3.2 | Optimal regressor Feature expansion Decision theory joint pdf slides |
Tutorial | | k-NN, Linear regression Gaussian process regression Training, validation and test set pdf slides |
Week 4 | | |
Lecture: Monday, Jan 30 | | Recap decision theory Logistic regression Neural networks pdf slides |
Lecture: Thursday, Feb 2 | Neural networks: Bishop 2006, Chap. 5, MacKay 2003, Chap. 39-40, 44, Hastie et al 2013, Chap. 11
| Neural networks Backpropagation pdf slides |
Tutorial | | Logistic regression Backpropagation examples pdf slides |
Week 5 | | |
Lecture: Monday, Feb 6 | | Multi-class classification Learning feedforward neural networks pdf slides |
Lecture: Thursday, Feb 9 | Convolutional neural networks: cs231n course slides Transfer learning and fine-tuning: cs231n course slides | Bag-of-tricks for deep neural networks Types of neural networks: convolutional neural networks, recurrent neural networks pdf slides |
Assignment 2: Thursday, Feb 9 | Due date: Feb 27 midnight, 2017 logistic regression, neural networks | Assignment handout (updated Feb 18th) Download notMNIST dataset here Histogram of results |
Tutorial | | Sample midterm review Assignment 1 post-mortem |
Week 6 | | |
Lecture: Mon, Feb 13 | Bishop 9.1 and 12.1 | k-means clustering, dimensionality reduction |
Study: Thu, Feb 16 | | Study independently in the classroom, with instructor on hand for questions. Unstructured. |
Midterm: Thursday, Feb 16 | Time: 6:20-7:50 pm. | Sample midterm from 2016 Midterm cheatsheet template Histogram of results |
Tutorial | | Midterm exam post-mortem |
Week 7 | | |
Lecture: Mon, Feb 27 | Bishop 3.3 Murphy 2012: parts of chap. 5 & sec. 7.6 | PCA continued, Bayesian methods |
Lecture: Thu, Mar 2 | Bishop 1.2.6 (Bayesian prediction), 1.3 (model selection), 2.4.2 (conjugate prior) | Bayesian learning continued |
Tutorial | | Examples of PCA, k-Means Bayesian predictive distribution Bayesian model comparison pdf slides |
Week 8 | | |
Lecture: Mon, Mar 6 | Mixture of Gaussians: Bishop 9.2 EM algorithm: Bishop 9.3 | Mixture models, EM algorithm |
Assignment 3: Wed, Mar 8 | Due date: March 24 midnight, 2017 Unsupervised learning, probablistic models | Assignment handout (updated Mar 13th) Download the datasets: data2D, data100D, tinymnist Download the utility function here |
Lecture: Thu, Mar 9 | Naive Bayes: Hastie et al 2013, Chap. 6.6.3 Bayesian network: Bishop 8.1, 8.2 | Mixture of Gaussians, Naive Bayes and Bayesian Networks |
Tutorial | | Introducing A3 Examples of Mixture of Bernoullis EM algorithm pdf slides |
Week 9 | | |
Lecture: Mon, Mar 13 | Bishop 8.1 & 8.2 Parts of Murphy Ch. 10 Russell and Norvig 2009 (AI: A Modern Approach) parts of Ch. 14 | Bayesian networks continued |
Lecture: Thu, Mar 16 | Bishop 8.3, 8.4.3 | Markov Random Fields, factor graphs |
Tutorial | | Review of graphical models Conversion between BN, MRF and FG Inference in graphical models pdf slides |
Week 10 | | |
Lecture: Mon, Mar 20 | Russell & Norvig 15.1 Parts of Bishop Chap. 13 | Sequence models |
Lecture: Thu, Mar 23 | Parts of Russell & Norvig 15.3 Parts of Bishop Chap. 13 | Hidden Markov Models (HMMs) |
Tutorial | | Review of Markov models Examples of inference in graphical models pdf slides |
Week 11 | | |
Lecture: Mon, Mar 27 | Murphy 17.4 End of Russell & Norvig 15.2 Bishop 13.2.5 | HMM inference/learning |
Assignment 4: Mon, Mar 27 | Due date: April 9th midnight, 2017 Graphical models, sum-product algorithm | Assignment handout (updated) |
Lecture: Thu, Mar 30 | Parts of MacKay Chapter 16 and Sections 26.1-26.2 Bishop 8.4.4 Kschischang, Frey and Loeliger: Factor Graphs and the Sum-Product Algorithm Section 2 Frey: Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models Section 2 | Message-passing algorithms (updated notation) |
Tutorial | | Forward-backward algorithm The sum-product algorithm pdf slides |
Week 12 | | |
Lecture: Mon, Apr 3 | Murphy 17.4.4 & 20.2 Bishop 8.4.5 & 13.2.5 MacKay 26.3 | Max-sum algorithm |
Lecture: Thu, Apr 6 | MacKay Chapters 16 and 26 Bishop 8.4.7 LBP: MacKay 26.4, Bishop 8.4.7 | Junction-tree algorithm, Loopy belief propagation |
Tutorial | | Review pdf slides |
Week 13 | | |
Lecture: Mon, Apr 10 | The first section of Murphy 19.6 | Supervised Learning using Graphical Models Discriminative Approach Conditional Random Fields (CRFs) Combining Deep Learning with Graphical Models |
Lecture: Thu, Apr 13 | All the above | Course concepts, the 2013 midterm, and finishing our junction-tree algorithm example |
Exam | | |
Thu, Apr 20 | | For study practice: 2013 midterm |