Foundation of Machine learning

Syllabus for Faculty Development Program: Foundations of machine learning in psychology  

Duration: 3 Months

Fees: 6k

Eligibility criteria:Working on research projects using psychological instruments , Basic understanding of psychology. 

Eligibility: Post graduation in psychology and social science related fields.

Learning Outcomes: students will learn principles of machine learning by the end of this course,  will be proficient in using SPSS to perform Supervised and unsupervised machine learning, interpret and report the result, develop skills in visualizing and presenting findings. 

Total marks: 100

Grading Criteria: 

A Excellent: 85 percent and above

B Good:72 to 84 percent 

C Satisfactory:50 to 69 percent 

D Needs improvement: Below 50 percent 

Classes per Week: 3 (1 Hour Each)

Target Audience: Faculty members in psychology and related disciplines

Prerequisites: Basic knowledge of psychology and Should have SPSS in laptop.

Enrollment Requirement: Entrance Examination for course: Interested candidates have to to register by paying 100 Rs for entrance exam. Candidates will be selected based upon the entrance exam result.

Pedagogy: Flipped classroom model, students will be given learning material before the class, hands on experience in class, work at home for assignments.

Foundations of machine learning in psychology 

Unit 1: Introduction to machine learning

Unit 2: Data Preparation for Machine learning with SPSS

Unit 3: Supervised learning

Unit 4: Unsupervised learning 

Unit 5: Project (Weeks 1–3)

Unit 1: Introduction to machine learning

1.1 Definition, types  of Machine Learning

1.2 Key concepts and their  Psychological Applications.

1.3. Differences between traditional statistics and machine learning.

Unit 2: Data Preparation for Machine learning with SPSS

2.1 Variable Transformation: Recoding, Dummy Variable and Standardization 

2.2 Splitting Data Sets: Training and Testing data, Handling missing data,Creating new variables.

2.3 Introduction to exploratory data analysis(EDA) and Data cleaning.

Unit 3: Supervised learning: Logistic Regression 

3.1 Fundamentals of Logistic Regression: difference between linear and logistic regression and applications of psychology.

3.2 Key concepts of Logistic Regression

3.3 Logistic Regression Using SPSS

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Unit 4: Unsupervised Learning: K-Means Clustering

4.1 Overview of unsupervised learning and applications, Mathematical Foundations: K-Means algorithm, Distance Metrics.

4.2 K-Means Clustering in SPSS: Setting up the data and running K-Means clustering, output interpretation.

4.3 Evaluating and interpreting K-Means clustering result 

Unit 5: Project

5.1. Project proposal writing 

5.2. Project on Logistic Regression 

53. Project K-Means clustering 

Assessment Structure

Weekly Assignments: To reinforce theoretical and practical knowledge.30 percent, per paper 6 marks 

Final exam : 40 percent  To test understanding of core concepts.

Final Project: 30 percent for  Comprehensive project showcasing the integration of machine learning techniques in psychology.

This structured syllabus ensures systematic learning and application of machine learning techniques in psychological research. The inclusion of practical sessions, project work, and theory-driven discussions makes it ideal for faculty development.