Course Syllabus
CS171 / Introduction to Machine Learning, Fall 2022, Course Schedule
The schedule is subject to change with fair notice communicated via Canvas course page
Course Schedule:
|
Week |
Date |
Topics, Readings, Assignments, Deadlines |
|
1 |
08/22 |
Introduction |
|
2 |
08/24 |
Why Machine Learning |
|
2 |
08/29 |
Supervised Learning: Classification, Regression, Generalization and Model Complexity |
|
3 |
09/31 |
Supervised Learning: K Nearest Neighbors, Linear Models, Naïve Bayes |
|
3 |
09/05 |
Labor Day (Campus Closed) |
|
4 |
09/07 |
Supervised Learning: Decision Trees, Ensembles DTs, Kernelized SVM |
|
4 |
09/12 |
Supervised Learning: Neural Networks Fundamentals |
|
5 |
09/14 |
Supervised Learning: Deep Learning |
|
5 |
09/19 |
Supervised Learning: Deep Learning with Convolutional Neural Networks |
|
6 |
09/21 |
Supervised Learning: Deep Learning with Recurrent Neural Networks |
|
6 |
09/26 |
Supervised Learning: Some Applications of Deep Learning |
|
7 |
09/28 |
Unsupervised Learning and Preprocessing: Principal Components Analysis |
|
7 |
10/03 |
Unsupervised Learning and Preprocessing: Non-negative Matrix Factorization |
|
8 |
10/05 |
Unsupervised Learning and Preprocessing: Manifold Learning with t-SNE |
|
8 |
10/10 |
Unsupervised Learning and Preprocessing: K-Means and K-Means++ |
|
9 |
10/12 |
Unsupervised Learning and Preprocessing: Agglomerative Clustering, DBSCAN |
|
9 |
10/17 |
Exam 1 |
|
10 |
10/19 |
Representing Data and Engineering Features: Categorical Variables |
|
10 |
10/24 |
Representing Data and Engineering Features: Binning, Discretization, Linear Models and Trees |
|
11 |
10/26 |
Representing Data and Engineering Features: Interactions, Polynomials, Automatic Feature Selection |
|
11 |
10/31 |
Model Evaluation and Improvement: Cross Validation |
|
12 |
11/02 |
Algorithmic Chains and Pipelines |
|
12 |
11/07 |
Working with Text Data: Types of Data, Representation as Bag-of-Words |
|
13 |
11/09 |
Working with Text Data: Stopwords, Rescaling, Model Coefficients |
|
13 |
11/14 |
Working with Text Data: n-Grams, Advanced Tokenization, Stemming and Lemmatization |
|
14 |
11/16 |
Exam 2 |
|
14 |
11/21 |
Reinforcement Learning: Introduction |
|
15 |
11/23 |
Reinforcement Learning: Tabular Solution Methods |
|
15 |
11/28 |
Reinforcement Learning: Approximate Solution Methods |
|
16 |
11/30 |
Wrap Up |
|
16 |
12/05 |
Project Presentations: Monday, December 05, 10:30 AM – 11:45 AM |
Nominal Grading Scale:
|
Percentage |
Grade |
|
97 – 100 plus |
A+ |
|
93 – 96 |
A |
|
90 – 92 |
A- |
|
87 – 89 |
B+ |
|
83 – 86 |
B |
|
80 – 82 |
B- |
|
77– 79 |
C+ |
|
73 – 76 |
C |
|
70 – 72 |
C- |
|
67 – 69 |
D+ |
|
63 – 66 |
D |
|
60 - 62 |
D- |
|
0-59 |
F |
Course Summary:
| Date | Details | Due |
|---|---|---|