COMP-551: Topics in Computer Science: Applied Machine Learning

Tentative Schedule for Fall 2018

Lec.
Date
Topic
Lecture Material
Reference
Readings
1
Sep. 05

Introduction to machine learning.
Logistics, Slides, Notes, Scribbles Bishop section 1.1 Computing Machinery and Intelligence
Probability Review-1
Probability Review-2
Linear Algebra Review
2
Sep. 10

Introduction to machine learning.
Notes, Scribbles Bishop section 1.1,
HTF section 2.3
3
Sep. 12

Statistical Decision Theory.
Notes, Scribbles HTF section 2.4, 2.5 Singular Value Decomposition.
4
Sep. 17

Linear Regression, Gradient Descent.
Notes, Scribbles Bishop section 3.1
5
Sep. 19

Bias-variance tradeoff, Regularization.
Notes Bishop section 1.5.5, 3.1.4, 3.2
HTF section 3.4.1, 3.4.2
Linear Algebra review (7.1) in this pdf
Lagrange Multiplier (Appendix E in Bishop)
6
Sep. 24

Linear Classification, Indicator Regression.
7
Sep. 26

PCA, LDA.
8
Oct. 01

GLMs, GDA, Evaluation metrics for classification.
9
Oct. 03

Naive Bayes, Logistic Regression.
Oct. 08

Thanksgiving.
10
Oct. 10

Newton-Raphson Method, Perceptron.
11
Oct. 15

Separating hyperplane, SVM.
12
Oct. 17

SVM.
13
Oct. 22

Non parametric Methods, Decision Trees.
14
Oct. 24

Ensemble Learning: Bagging, Boosting.
15
Oct. 29

Stacking, Neural Networks.
16
Oct. 31

Neural Networks and Backpropagation.
17
Nov. 05

Training Deep Neural Nets.
18
Nov. 07

Convnets, RNNs.
19
Nov. 12

RNNs, Optimization.
20
Nov. 14

MLE, MAP, Bayesian Learning.
21
Nov. 19

Bayesian Linear Regression.
22
Nov. 21

Kernel Methods, Gaussian Processes.
23
Nov. 26

Clustering, K-means, DBScan.
24
Nov. 28

Gaussian Mixture Models, EM Algorithm.
25
Dec. 03

Course Summary and Practical Tips.
26
Dec. 04

Frontiers in ML, What Next?

Reference Materials

  1. [HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Available free online.
  2. [Bishop] Christopher Bishop. Pattern Recognition and Machine Learning.
  3. [Mitchell] Tom Mitchell. Machine Learning.
  4. [TSKK] Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. Introduction to Data Mining.
  5. [Rojas] Raul Rojas. Neural Networks.
  6. [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. Available free online.